authors: “Sameer Nair-Desai, Rashad Ahmed, Weining Xin” date: “5/22/2020” output: html_document: default pdf_document: default word_document: default —
If you do not wish to run the code, please see the final output (graphs and datasets) provided in the Github repository. Make sure to check the relevant README files for the contents of the folder.
Installing the relevant packages. install.packages(“sf”) install.packages(“readr”) install.packages(“tmap”) install.packages(“leaflet”) install.packages(“mapview”) install.packages(“ggplot2”) install.packages(“tidyverse”) install.packages(“rlang”) install.packages(“reshape”) install.packages(“rgdal”) install.packages(“lubridate”) install.packages(“plotly”) install.packages(“patchwork”) install.packages(“ggforce”) install.packages(“gridExtra”) install.packages(“htmltools”) install.packages(“data.table”) install.packages(“webshot”) webshot::install_phantomjs() install.packages(“runner”) install.packages(“zoo”) install.packages(“devtools”) devtools::install_version(“latticeExtra”, version=“0.6-28”) install.packages(“Hmisc”) install.packages(“DataCombine”) install.packages(“fastDummies”) install.packages(“heatmaply”) install.packages(“glmnet”) install.packages(“caret”) install.packages(“summarytools”) install.packages(“remote”) remotes::install_github(‘rapporter/pander’) install.packages(“mlbench”) install.packages(“psych”) install.packages(“lmtest”) install.packages(“quantmod”) install.packages(“corrplot”) install.packages(“fBasics”) install.packages(“stargazer”) install.packages(“tseries”) install.packages(“vars”) install.packages(“gghighlight”)
# Setting our initial libraries. In subsequent rounds of analysis, we will be integrating new libraries (we do not do so here to avoid conflicts between packages).
library(sf)
## Warning: package 'sf' was built under R version 3.6.2
## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0
library(readr)
library(mapview)
## Warning: package 'mapview' was built under R version 3.6.2
library(ggplot2)
library(tidyverse)
## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ tibble 3.0.1 ✓ dplyr 0.8.5
## ✓ tidyr 1.0.3 ✓ stringr 1.4.0
## ✓ purrr 0.3.4 ✓ forcats 0.5.0
## Warning: package 'tibble' was built under R version 3.6.2
## Warning: package 'tidyr' was built under R version 3.6.2
## Warning: package 'purrr' was built under R version 3.6.2
## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(rlang)
## Warning: package 'rlang' was built under R version 3.6.2
##
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
##
## %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
## flatten_lgl, flatten_raw, invoke, list_along, modify, prepend,
## splice
library(reshape)
##
## Attaching package: 'reshape'
## The following object is masked from 'package:dplyr':
##
## rename
## The following objects are masked from 'package:tidyr':
##
## expand, smiths
library(rgdal)
## Warning: package 'rgdal' was built under R version 3.6.2
## Loading required package: sp
## rgdal: version: 1.5-8, (SVN revision 990)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.4.2, released 2019/06/28
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/gdal
## GDAL binary built with GEOS: FALSE
## Loaded PROJ runtime: Rel. 5.2.0, September 15th, 2018, [PJ_VERSION: 520]
## Path to PROJ shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/proj
## Linking to sp version:1.4-2
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
library(lubridate)
## Warning: package 'lubridate' was built under R version 3.6.2
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:reshape':
##
## stamp
## The following objects are masked from 'package:dplyr':
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## intersect, setdiff, union
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(plotly)
## Warning: package 'plotly' was built under R version 3.6.2
##
## Attaching package: 'plotly'
## The following object is masked from 'package:reshape':
##
## rename
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
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## filter
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## layout
library(patchwork)
library(ggforce)
library(gridExtra)
##
## Attaching package: 'gridExtra'
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## combine
library(htmltools)
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:lubridate':
##
## hour, isoweek, mday, minute, month, quarter, second, wday, week,
## yday, year
## The following object is masked from 'package:reshape':
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## melt
## The following object is masked from 'package:rlang':
##
## :=
## The following objects are masked from 'package:dplyr':
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## The following object is masked from 'package:purrr':
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## transpose
library(webshot)
library(coronavirus)
## Warning: package 'coronavirus' was built under R version 3.6.2
library(runner)
## Warning: package 'runner' was built under R version 3.6.2
library(zoo)
## Warning: package 'zoo' was built under R version 3.6.2
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(DataCombine)
##
## Attaching package: 'DataCombine'
## The following object is masked from 'package:data.table':
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## shift
library(fastDummies)
library(car)
## Warning: package 'car' was built under R version 3.6.2
## Loading required package: carData
## Warning: package 'carData' was built under R version 3.6.2
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
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## recode
## The following object is masked from 'package:purrr':
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## some
library(heatmaply)
## Warning: package 'heatmaply' was built under R version 3.6.2
## Loading required package: viridis
## Loading required package: viridisLite
## Registered S3 method overwritten by 'seriation':
## method from
## reorder.hclust gclus
##
## ======================
## Welcome to heatmaply version 1.1.0
##
## Type citation('heatmaply') for how to cite the package.
## Type ?heatmaply for the main documentation.
##
## The github page is: https://github.com/talgalili/heatmaply/
## Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
## Or contact: <tal.galili@gmail.com>
## ======================
library(htmlwidgets)
library(summarytools)
## Registered S3 method overwritten by 'pryr':
## method from
## print.bytes Rcpp
## For best results, restart R session and update pander using devtools:: or remotes::install_github('rapporter/pander')
##
## Attaching package: 'summarytools'
## The following object is masked from 'package:tibble':
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## view
library(glmnet)
## Warning: package 'glmnet' was built under R version 3.6.2
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following object is masked from 'package:reshape':
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## expand
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## expand, pack, unpack
## Loaded glmnet 4.0
library(caret)
## Loading required package: lattice
## Warning: package 'lattice' was built under R version 3.6.2
##
## Attaching package: 'caret'
## The following object is masked from 'package:purrr':
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## lift
library(mlbench)
library(psych)
##
## Attaching package: 'psych'
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## logit
## The following objects are masked from 'package:ggplot2':
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## %+%, alpha
library(plm)
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## Attaching package: 'plm'
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library(lmtest)
library(quantmod)
## Warning: package 'quantmod' was built under R version 3.6.2
## Loading required package: xts
##
## Attaching package: 'xts'
## The following objects are masked from 'package:data.table':
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## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
## Version 0.4-0 included new data defaults. See ?getSymbols.
library(leaflet)
##
## Attaching package: 'leaflet'
## The following object is masked from 'package:xts':
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## addLegend
library(corrplot)
## corrplot 0.84 loaded
library(fBasics)
## Loading required package: timeDate
## Loading required package: timeSeries
##
## Attaching package: 'timeSeries'
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## outlier
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## time<-
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## tr
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## densityPlot
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## getDescription
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
library(tseries)
library(vars)
## Loading required package: MASS
## Warning: package 'MASS' was built under R version 3.6.2
##
## Attaching package: 'MASS'
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## Loading required package: strucchange
## Loading required package: sandwich
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## boundary
## Loading required package: urca
library(dplyr)
library(gghighlight)
## Warning: package 'gghighlight' was built under R version 3.6.2
# I don't want scientific notation for my values, so I specify this below.
options(scipen = 999)
# Import and quickly cleaning our data. We will call the import our initial confirmed data (Wide).
Initial_Confirmed_Wide <- read_csv("data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## See spec(...) for full column specifications.
# Our data is in wide format. Here, I translate it to long format.
Confirmed_Long <- pivot_longer(Initial_Confirmed_Wide, cols = c(5:102), names_to = "Dates", values_to = "Confirmed_Cases")
# Now, I rename my column for country in the long dataset.
Confirmed_Long$COUNTRY <- Confirmed_Long$`Country/Region`
# I can also convert the dates into a more usable string format with lubridate.
Confirmed_Long$Date <- mdy(Confirmed_Long$Dates, quiet = FALSE, tz = NULL, locale = Sys.getlocale("LC_TIME"),
truncated = 0)
order(Confirmed_Long$Date)
## [1] 1 99 197 295 393 491 589 687 785 883 981 1079
## [13] 1177 1275 1373 1471 1569 1667 1765 1863 1961 2059 2157 2255
## [25] 2353 2451 2549 2647 2745 2843 2941 3039 3137 3235 3333 3431
## [37] 3529 3627 3725 3823 3921 4019 4117 4215 4313 4411 4509 4607
## [49] 4705 4803 4901 4999 5097 5195 5293 5391 5489 5587 5685 5783
## [61] 5881 5979 6077 6175 6273 6371 6469 6567 6665 6763 6861 6959
## [73] 7057 7155 7253 7351 7449 7547 7645 7743 7841 7939 8037 8135
## [85] 8233 8331 8429 8527 8625 8723 8821 8919 9017 9115 9213 9311
## [97] 9409 9507 9605 9703 9801 9899 9997 10095 10193 10291 10389 10487
## [109] 10585 10683 10781 10879 10977 11075 11173 11271 11369 11467 11565 11663
## [121] 11761 11859 11957 12055 12153 12251 12349 12447 12545 12643 12741 12839
## [133] 12937 13035 13133 13231 13329 13427 13525 13623 13721 13819 13917 14015
## [145] 14113 14211 14309 14407 14505 14603 14701 14799 14897 14995 15093 15191
## [157] 15289 15387 15485 15583 15681 15779 15877 15975 16073 16171 16269 16367
## [169] 16465 16563 16661 16759 16857 16955 17053 17151 17249 17347 17445 17543
## [181] 17641 17739 17837 17935 18033 18131 18229 18327 18425 18523 18621 18719
## [193] 18817 18915 19013 19111 19209 19307 19405 19503 19601 19699 19797 19895
## [205] 19993 20091 20189 20287 20385 20483 20581 20679 20777 20875 20973 21071
## [217] 21169 21267 21365 21463 21561 21659 21757 21855 21953 22051 22149 22247
## [229] 22345 22443 22541 22639 22737 22835 22933 23031 23129 23227 23325 23423
## [241] 23521 23619 23717 23815 23913 24011 24109 24207 24305 24403 24501 24599
## [253] 24697 24795 24893 24991 25089 25187 25285 25383 25481 25579 25677 25775
## [265] 2 100 198 296 394 492 590 688 786 884 982 1080
## [277] 1178 1276 1374 1472 1570 1668 1766 1864 1962 2060 2158 2256
## [289] 2354 2452 2550 2648 2746 2844 2942 3040 3138 3236 3334 3432
## [301] 3530 3628 3726 3824 3922 4020 4118 4216 4314 4412 4510 4608
## [313] 4706 4804 4902 5000 5098 5196 5294 5392 5490 5588 5686 5784
## [325] 5882 5980 6078 6176 6274 6372 6470 6568 6666 6764 6862 6960
## [337] 7058 7156 7254 7352 7450 7548 7646 7744 7842 7940 8038 8136
## [349] 8234 8332 8430 8528 8626 8724 8822 8920 9018 9116 9214 9312
## [361] 9410 9508 9606 9704 9802 9900 9998 10096 10194 10292 10390 10488
## [373] 10586 10684 10782 10880 10978 11076 11174 11272 11370 11468 11566 11664
## [385] 11762 11860 11958 12056 12154 12252 12350 12448 12546 12644 12742 12840
## [397] 12938 13036 13134 13232 13330 13428 13526 13624 13722 13820 13918 14016
## [409] 14114 14212 14310 14408 14506 14604 14702 14800 14898 14996 15094 15192
## [421] 15290 15388 15486 15584 15682 15780 15878 15976 16074 16172 16270 16368
## [433] 16466 16564 16662 16760 16858 16956 17054 17152 17250 17348 17446 17544
## [445] 17642 17740 17838 17936 18034 18132 18230 18328 18426 18524 18622 18720
## [457] 18818 18916 19014 19112 19210 19308 19406 19504 19602 19700 19798 19896
## [469] 19994 20092 20190 20288 20386 20484 20582 20680 20778 20876 20974 21072
## [481] 21170 21268 21366 21464 21562 21660 21758 21856 21954 22052 22150 22248
## [493] 22346 22444 22542 22640 22738 22836 22934 23032 23130 23228 23326 23424
## [505] 23522 23620 23718 23816 23914 24012 24110 24208 24306 24404 24502 24600
## [517] 24698 24796 24894 24992 25090 25188 25286 25384 25482 25580 25678 25776
## [529] 3 101 199 297 395 493 591 689 787 885 983 1081
## [541] 1179 1277 1375 1473 1571 1669 1767 1865 1963 2061 2159 2257
## [553] 2355 2453 2551 2649 2747 2845 2943 3041 3139 3237 3335 3433
## [565] 3531 3629 3727 3825 3923 4021 4119 4217 4315 4413 4511 4609
## [577] 4707 4805 4903 5001 5099 5197 5295 5393 5491 5589 5687 5785
## [589] 5883 5981 6079 6177 6275 6373 6471 6569 6667 6765 6863 6961
## [601] 7059 7157 7255 7353 7451 7549 7647 7745 7843 7941 8039 8137
## [613] 8235 8333 8431 8529 8627 8725 8823 8921 9019 9117 9215 9313
## [625] 9411 9509 9607 9705 9803 9901 9999 10097 10195 10293 10391 10489
## [637] 10587 10685 10783 10881 10979 11077 11175 11273 11371 11469 11567 11665
## [649] 11763 11861 11959 12057 12155 12253 12351 12449 12547 12645 12743 12841
## [661] 12939 13037 13135 13233 13331 13429 13527 13625 13723 13821 13919 14017
## [673] 14115 14213 14311 14409 14507 14605 14703 14801 14899 14997 15095 15193
## [685] 15291 15389 15487 15585 15683 15781 15879 15977 16075 16173 16271 16369
## [697] 16467 16565 16663 16761 16859 16957 17055 17153 17251 17349 17447 17545
## [709] 17643 17741 17839 17937 18035 18133 18231 18329 18427 18525 18623 18721
## [721] 18819 18917 19015 19113 19211 19309 19407 19505 19603 19701 19799 19897
## [733] 19995 20093 20191 20289 20387 20485 20583 20681 20779 20877 20975 21073
## [745] 21171 21269 21367 21465 21563 21661 21759 21857 21955 22053 22151 22249
## [757] 22347 22445 22543 22641 22739 22837 22935 23033 23131 23229 23327 23425
## [769] 23523 23621 23719 23817 23915 24013 24111 24209 24307 24405 24503 24601
## [781] 24699 24797 24895 24993 25091 25189 25287 25385 25483 25581 25679 25777
## [793] 4 102 200 298 396 494 592 690 788 886 984 1082
## [805] 1180 1278 1376 1474 1572 1670 1768 1866 1964 2062 2160 2258
## [817] 2356 2454 2552 2650 2748 2846 2944 3042 3140 3238 3336 3434
## [829] 3532 3630 3728 3826 3924 4022 4120 4218 4316 4414 4512 4610
## [841] 4708 4806 4904 5002 5100 5198 5296 5394 5492 5590 5688 5786
## [853] 5884 5982 6080 6178 6276 6374 6472 6570 6668 6766 6864 6962
## [865] 7060 7158 7256 7354 7452 7550 7648 7746 7844 7942 8040 8138
## [877] 8236 8334 8432 8530 8628 8726 8824 8922 9020 9118 9216 9314
## [889] 9412 9510 9608 9706 9804 9902 10000 10098 10196 10294 10392 10490
## [901] 10588 10686 10784 10882 10980 11078 11176 11274 11372 11470 11568 11666
## [913] 11764 11862 11960 12058 12156 12254 12352 12450 12548 12646 12744 12842
## [925] 12940 13038 13136 13234 13332 13430 13528 13626 13724 13822 13920 14018
## [937] 14116 14214 14312 14410 14508 14606 14704 14802 14900 14998 15096 15194
## [949] 15292 15390 15488 15586 15684 15782 15880 15978 16076 16174 16272 16370
## [961] 16468 16566 16664 16762 16860 16958 17056 17154 17252 17350 17448 17546
## [973] 17644 17742 17840 17938 18036 18134 18232 18330 18428 18526 18624 18722
## [985] 18820 18918 19016 19114 19212 19310 19408 19506 19604 19702 19800 19898
## [997] 19996 20094 20192 20290 20388 20486 20584 20682 20780 20878 20976 21074
## [1009] 21172 21270 21368 21466 21564 21662 21760 21858 21956 22054 22152 22250
## [1021] 22348 22446 22544 22642 22740 22838 22936 23034 23132 23230 23328 23426
## [1033] 23524 23622 23720 23818 23916 24014 24112 24210 24308 24406 24504 24602
## [1045] 24700 24798 24896 24994 25092 25190 25288 25386 25484 25582 25680 25778
## [1057] 5 103 201 299 397 495 593 691 789 887 985 1083
## [1069] 1181 1279 1377 1475 1573 1671 1769 1867 1965 2063 2161 2259
## [1081] 2357 2455 2553 2651 2749 2847 2945 3043 3141 3239 3337 3435
## [1093] 3533 3631 3729 3827 3925 4023 4121 4219 4317 4415 4513 4611
## [1105] 4709 4807 4905 5003 5101 5199 5297 5395 5493 5591 5689 5787
## [1117] 5885 5983 6081 6179 6277 6375 6473 6571 6669 6767 6865 6963
## [1129] 7061 7159 7257 7355 7453 7551 7649 7747 7845 7943 8041 8139
## [1141] 8237 8335 8433 8531 8629 8727 8825 8923 9021 9119 9217 9315
## [1153] 9413 9511 9609 9707 9805 9903 10001 10099 10197 10295 10393 10491
## [1165] 10589 10687 10785 10883 10981 11079 11177 11275 11373 11471 11569 11667
## [1177] 11765 11863 11961 12059 12157 12255 12353 12451 12549 12647 12745 12843
## [1189] 12941 13039 13137 13235 13333 13431 13529 13627 13725 13823 13921 14019
## [1201] 14117 14215 14313 14411 14509 14607 14705 14803 14901 14999 15097 15195
## [1213] 15293 15391 15489 15587 15685 15783 15881 15979 16077 16175 16273 16371
## [1225] 16469 16567 16665 16763 16861 16959 17057 17155 17253 17351 17449 17547
## [1237] 17645 17743 17841 17939 18037 18135 18233 18331 18429 18527 18625 18723
## [1249] 18821 18919 19017 19115 19213 19311 19409 19507 19605 19703 19801 19899
## [1261] 19997 20095 20193 20291 20389 20487 20585 20683 20781 20879 20977 21075
## [1273] 21173 21271 21369 21467 21565 21663 21761 21859 21957 22055 22153 22251
## [1285] 22349 22447 22545 22643 22741 22839 22937 23035 23133 23231 23329 23427
## [1297] 23525 23623 23721 23819 23917 24015 24113 24211 24309 24407 24505 24603
## [1309] 24701 24799 24897 24995 25093 25191 25289 25387 25485 25583 25681 25779
## [1321] 6 104 202 300 398 496 594 692 790 888 986 1084
## [1333] 1182 1280 1378 1476 1574 1672 1770 1868 1966 2064 2162 2260
## [1345] 2358 2456 2554 2652 2750 2848 2946 3044 3142 3240 3338 3436
## [1357] 3534 3632 3730 3828 3926 4024 4122 4220 4318 4416 4514 4612
## [1369] 4710 4808 4906 5004 5102 5200 5298 5396 5494 5592 5690 5788
## [1381] 5886 5984 6082 6180 6278 6376 6474 6572 6670 6768 6866 6964
## [1393] 7062 7160 7258 7356 7454 7552 7650 7748 7846 7944 8042 8140
## [1405] 8238 8336 8434 8532 8630 8728 8826 8924 9022 9120 9218 9316
## [1417] 9414 9512 9610 9708 9806 9904 10002 10100 10198 10296 10394 10492
## [1429] 10590 10688 10786 10884 10982 11080 11178 11276 11374 11472 11570 11668
## [1441] 11766 11864 11962 12060 12158 12256 12354 12452 12550 12648 12746 12844
## [1453] 12942 13040 13138 13236 13334 13432 13530 13628 13726 13824 13922 14020
## [1465] 14118 14216 14314 14412 14510 14608 14706 14804 14902 15000 15098 15196
## [1477] 15294 15392 15490 15588 15686 15784 15882 15980 16078 16176 16274 16372
## [1489] 16470 16568 16666 16764 16862 16960 17058 17156 17254 17352 17450 17548
## [1501] 17646 17744 17842 17940 18038 18136 18234 18332 18430 18528 18626 18724
## [1513] 18822 18920 19018 19116 19214 19312 19410 19508 19606 19704 19802 19900
## [1525] 19998 20096 20194 20292 20390 20488 20586 20684 20782 20880 20978 21076
## [1537] 21174 21272 21370 21468 21566 21664 21762 21860 21958 22056 22154 22252
## [1549] 22350 22448 22546 22644 22742 22840 22938 23036 23134 23232 23330 23428
## [1561] 23526 23624 23722 23820 23918 24016 24114 24212 24310 24408 24506 24604
## [1573] 24702 24800 24898 24996 25094 25192 25290 25388 25486 25584 25682 25780
## [1585] 7 105 203 301 399 497 595 693 791 889 987 1085
## [1597] 1183 1281 1379 1477 1575 1673 1771 1869 1967 2065 2163 2261
## [1609] 2359 2457 2555 2653 2751 2849 2947 3045 3143 3241 3339 3437
## [1621] 3535 3633 3731 3829 3927 4025 4123 4221 4319 4417 4515 4613
## [1633] 4711 4809 4907 5005 5103 5201 5299 5397 5495 5593 5691 5789
## [1645] 5887 5985 6083 6181 6279 6377 6475 6573 6671 6769 6867 6965
## [1657] 7063 7161 7259 7357 7455 7553 7651 7749 7847 7945 8043 8141
## [1669] 8239 8337 8435 8533 8631 8729 8827 8925 9023 9121 9219 9317
## [1681] 9415 9513 9611 9709 9807 9905 10003 10101 10199 10297 10395 10493
## [1693] 10591 10689 10787 10885 10983 11081 11179 11277 11375 11473 11571 11669
## [1705] 11767 11865 11963 12061 12159 12257 12355 12453 12551 12649 12747 12845
## [1717] 12943 13041 13139 13237 13335 13433 13531 13629 13727 13825 13923 14021
## [1729] 14119 14217 14315 14413 14511 14609 14707 14805 14903 15001 15099 15197
## [1741] 15295 15393 15491 15589 15687 15785 15883 15981 16079 16177 16275 16373
## [1753] 16471 16569 16667 16765 16863 16961 17059 17157 17255 17353 17451 17549
## [1765] 17647 17745 17843 17941 18039 18137 18235 18333 18431 18529 18627 18725
## [1777] 18823 18921 19019 19117 19215 19313 19411 19509 19607 19705 19803 19901
## [1789] 19999 20097 20195 20293 20391 20489 20587 20685 20783 20881 20979 21077
## [1801] 21175 21273 21371 21469 21567 21665 21763 21861 21959 22057 22155 22253
## [1813] 22351 22449 22547 22645 22743 22841 22939 23037 23135 23233 23331 23429
## [1825] 23527 23625 23723 23821 23919 24017 24115 24213 24311 24409 24507 24605
## [1837] 24703 24801 24899 24997 25095 25193 25291 25389 25487 25585 25683 25781
## [1849] 8 106 204 302 400 498 596 694 792 890 988 1086
## [1861] 1184 1282 1380 1478 1576 1674 1772 1870 1968 2066 2164 2262
## [1873] 2360 2458 2556 2654 2752 2850 2948 3046 3144 3242 3340 3438
## [1885] 3536 3634 3732 3830 3928 4026 4124 4222 4320 4418 4516 4614
## [1897] 4712 4810 4908 5006 5104 5202 5300 5398 5496 5594 5692 5790
## [1909] 5888 5986 6084 6182 6280 6378 6476 6574 6672 6770 6868 6966
## [1921] 7064 7162 7260 7358 7456 7554 7652 7750 7848 7946 8044 8142
## [1933] 8240 8338 8436 8534 8632 8730 8828 8926 9024 9122 9220 9318
## [1945] 9416 9514 9612 9710 9808 9906 10004 10102 10200 10298 10396 10494
## [1957] 10592 10690 10788 10886 10984 11082 11180 11278 11376 11474 11572 11670
## [1969] 11768 11866 11964 12062 12160 12258 12356 12454 12552 12650 12748 12846
## [1981] 12944 13042 13140 13238 13336 13434 13532 13630 13728 13826 13924 14022
## [1993] 14120 14218 14316 14414 14512 14610 14708 14806 14904 15002 15100 15198
## [2005] 15296 15394 15492 15590 15688 15786 15884 15982 16080 16178 16276 16374
## [2017] 16472 16570 16668 16766 16864 16962 17060 17158 17256 17354 17452 17550
## [2029] 17648 17746 17844 17942 18040 18138 18236 18334 18432 18530 18628 18726
## [2041] 18824 18922 19020 19118 19216 19314 19412 19510 19608 19706 19804 19902
## [2053] 20000 20098 20196 20294 20392 20490 20588 20686 20784 20882 20980 21078
## [2065] 21176 21274 21372 21470 21568 21666 21764 21862 21960 22058 22156 22254
## [2077] 22352 22450 22548 22646 22744 22842 22940 23038 23136 23234 23332 23430
## [2089] 23528 23626 23724 23822 23920 24018 24116 24214 24312 24410 24508 24606
## [2101] 24704 24802 24900 24998 25096 25194 25292 25390 25488 25586 25684 25782
## [2113] 9 107 205 303 401 499 597 695 793 891 989 1087
## [2125] 1185 1283 1381 1479 1577 1675 1773 1871 1969 2067 2165 2263
## [2137] 2361 2459 2557 2655 2753 2851 2949 3047 3145 3243 3341 3439
## [2149] 3537 3635 3733 3831 3929 4027 4125 4223 4321 4419 4517 4615
## [2161] 4713 4811 4909 5007 5105 5203 5301 5399 5497 5595 5693 5791
## [2173] 5889 5987 6085 6183 6281 6379 6477 6575 6673 6771 6869 6967
## [2185] 7065 7163 7261 7359 7457 7555 7653 7751 7849 7947 8045 8143
## [2197] 8241 8339 8437 8535 8633 8731 8829 8927 9025 9123 9221 9319
## [2209] 9417 9515 9613 9711 9809 9907 10005 10103 10201 10299 10397 10495
## [2221] 10593 10691 10789 10887 10985 11083 11181 11279 11377 11475 11573 11671
## [2233] 11769 11867 11965 12063 12161 12259 12357 12455 12553 12651 12749 12847
## [2245] 12945 13043 13141 13239 13337 13435 13533 13631 13729 13827 13925 14023
## [2257] 14121 14219 14317 14415 14513 14611 14709 14807 14905 15003 15101 15199
## [2269] 15297 15395 15493 15591 15689 15787 15885 15983 16081 16179 16277 16375
## [2281] 16473 16571 16669 16767 16865 16963 17061 17159 17257 17355 17453 17551
## [2293] 17649 17747 17845 17943 18041 18139 18237 18335 18433 18531 18629 18727
## [2305] 18825 18923 19021 19119 19217 19315 19413 19511 19609 19707 19805 19903
## [2317] 20001 20099 20197 20295 20393 20491 20589 20687 20785 20883 20981 21079
## [2329] 21177 21275 21373 21471 21569 21667 21765 21863 21961 22059 22157 22255
## [2341] 22353 22451 22549 22647 22745 22843 22941 23039 23137 23235 23333 23431
## [2353] 23529 23627 23725 23823 23921 24019 24117 24215 24313 24411 24509 24607
## [2365] 24705 24803 24901 24999 25097 25195 25293 25391 25489 25587 25685 25783
## [2377] 10 108 206 304 402 500 598 696 794 892 990 1088
## [2389] 1186 1284 1382 1480 1578 1676 1774 1872 1970 2068 2166 2264
## [2401] 2362 2460 2558 2656 2754 2852 2950 3048 3146 3244 3342 3440
## [2413] 3538 3636 3734 3832 3930 4028 4126 4224 4322 4420 4518 4616
## [2425] 4714 4812 4910 5008 5106 5204 5302 5400 5498 5596 5694 5792
## [2437] 5890 5988 6086 6184 6282 6380 6478 6576 6674 6772 6870 6968
## [2449] 7066 7164 7262 7360 7458 7556 7654 7752 7850 7948 8046 8144
## [2461] 8242 8340 8438 8536 8634 8732 8830 8928 9026 9124 9222 9320
## [2473] 9418 9516 9614 9712 9810 9908 10006 10104 10202 10300 10398 10496
## [2485] 10594 10692 10790 10888 10986 11084 11182 11280 11378 11476 11574 11672
## [2497] 11770 11868 11966 12064 12162 12260 12358 12456 12554 12652 12750 12848
## [2509] 12946 13044 13142 13240 13338 13436 13534 13632 13730 13828 13926 14024
## [2521] 14122 14220 14318 14416 14514 14612 14710 14808 14906 15004 15102 15200
## [2533] 15298 15396 15494 15592 15690 15788 15886 15984 16082 16180 16278 16376
## [2545] 16474 16572 16670 16768 16866 16964 17062 17160 17258 17356 17454 17552
## [2557] 17650 17748 17846 17944 18042 18140 18238 18336 18434 18532 18630 18728
## [2569] 18826 18924 19022 19120 19218 19316 19414 19512 19610 19708 19806 19904
## [2581] 20002 20100 20198 20296 20394 20492 20590 20688 20786 20884 20982 21080
## [2593] 21178 21276 21374 21472 21570 21668 21766 21864 21962 22060 22158 22256
## [2605] 22354 22452 22550 22648 22746 22844 22942 23040 23138 23236 23334 23432
## [2617] 23530 23628 23726 23824 23922 24020 24118 24216 24314 24412 24510 24608
## [2629] 24706 24804 24902 25000 25098 25196 25294 25392 25490 25588 25686 25784
## [2641] 11 109 207 305 403 501 599 697 795 893 991 1089
## [2653] 1187 1285 1383 1481 1579 1677 1775 1873 1971 2069 2167 2265
## [2665] 2363 2461 2559 2657 2755 2853 2951 3049 3147 3245 3343 3441
## [2677] 3539 3637 3735 3833 3931 4029 4127 4225 4323 4421 4519 4617
## [2689] 4715 4813 4911 5009 5107 5205 5303 5401 5499 5597 5695 5793
## [2701] 5891 5989 6087 6185 6283 6381 6479 6577 6675 6773 6871 6969
## [2713] 7067 7165 7263 7361 7459 7557 7655 7753 7851 7949 8047 8145
## [2725] 8243 8341 8439 8537 8635 8733 8831 8929 9027 9125 9223 9321
## [2737] 9419 9517 9615 9713 9811 9909 10007 10105 10203 10301 10399 10497
## [2749] 10595 10693 10791 10889 10987 11085 11183 11281 11379 11477 11575 11673
## [2761] 11771 11869 11967 12065 12163 12261 12359 12457 12555 12653 12751 12849
## [2773] 12947 13045 13143 13241 13339 13437 13535 13633 13731 13829 13927 14025
## [2785] 14123 14221 14319 14417 14515 14613 14711 14809 14907 15005 15103 15201
## [2797] 15299 15397 15495 15593 15691 15789 15887 15985 16083 16181 16279 16377
## [2809] 16475 16573 16671 16769 16867 16965 17063 17161 17259 17357 17455 17553
## [2821] 17651 17749 17847 17945 18043 18141 18239 18337 18435 18533 18631 18729
## [2833] 18827 18925 19023 19121 19219 19317 19415 19513 19611 19709 19807 19905
## [2845] 20003 20101 20199 20297 20395 20493 20591 20689 20787 20885 20983 21081
## [2857] 21179 21277 21375 21473 21571 21669 21767 21865 21963 22061 22159 22257
## [2869] 22355 22453 22551 22649 22747 22845 22943 23041 23139 23237 23335 23433
## [2881] 23531 23629 23727 23825 23923 24021 24119 24217 24315 24413 24511 24609
## [2893] 24707 24805 24903 25001 25099 25197 25295 25393 25491 25589 25687 25785
## [2905] 12 110 208 306 404 502 600 698 796 894 992 1090
## [2917] 1188 1286 1384 1482 1580 1678 1776 1874 1972 2070 2168 2266
## [2929] 2364 2462 2560 2658 2756 2854 2952 3050 3148 3246 3344 3442
## [2941] 3540 3638 3736 3834 3932 4030 4128 4226 4324 4422 4520 4618
## [2953] 4716 4814 4912 5010 5108 5206 5304 5402 5500 5598 5696 5794
## [2965] 5892 5990 6088 6186 6284 6382 6480 6578 6676 6774 6872 6970
## [2977] 7068 7166 7264 7362 7460 7558 7656 7754 7852 7950 8048 8146
## [2989] 8244 8342 8440 8538 8636 8734 8832 8930 9028 9126 9224 9322
## [3001] 9420 9518 9616 9714 9812 9910 10008 10106 10204 10302 10400 10498
## [3013] 10596 10694 10792 10890 10988 11086 11184 11282 11380 11478 11576 11674
## [3025] 11772 11870 11968 12066 12164 12262 12360 12458 12556 12654 12752 12850
## [3037] 12948 13046 13144 13242 13340 13438 13536 13634 13732 13830 13928 14026
## [3049] 14124 14222 14320 14418 14516 14614 14712 14810 14908 15006 15104 15202
## [3061] 15300 15398 15496 15594 15692 15790 15888 15986 16084 16182 16280 16378
## [3073] 16476 16574 16672 16770 16868 16966 17064 17162 17260 17358 17456 17554
## [3085] 17652 17750 17848 17946 18044 18142 18240 18338 18436 18534 18632 18730
## [3097] 18828 18926 19024 19122 19220 19318 19416 19514 19612 19710 19808 19906
## [3109] 20004 20102 20200 20298 20396 20494 20592 20690 20788 20886 20984 21082
## [3121] 21180 21278 21376 21474 21572 21670 21768 21866 21964 22062 22160 22258
## [3133] 22356 22454 22552 22650 22748 22846 22944 23042 23140 23238 23336 23434
## [3145] 23532 23630 23728 23826 23924 24022 24120 24218 24316 24414 24512 24610
## [3157] 24708 24806 24904 25002 25100 25198 25296 25394 25492 25590 25688 25786
## [3169] 13 111 209 307 405 503 601 699 797 895 993 1091
## [3181] 1189 1287 1385 1483 1581 1679 1777 1875 1973 2071 2169 2267
## [3193] 2365 2463 2561 2659 2757 2855 2953 3051 3149 3247 3345 3443
## [3205] 3541 3639 3737 3835 3933 4031 4129 4227 4325 4423 4521 4619
## [3217] 4717 4815 4913 5011 5109 5207 5305 5403 5501 5599 5697 5795
## [3229] 5893 5991 6089 6187 6285 6383 6481 6579 6677 6775 6873 6971
## [3241] 7069 7167 7265 7363 7461 7559 7657 7755 7853 7951 8049 8147
## [3253] 8245 8343 8441 8539 8637 8735 8833 8931 9029 9127 9225 9323
## [3265] 9421 9519 9617 9715 9813 9911 10009 10107 10205 10303 10401 10499
## [3277] 10597 10695 10793 10891 10989 11087 11185 11283 11381 11479 11577 11675
## [3289] 11773 11871 11969 12067 12165 12263 12361 12459 12557 12655 12753 12851
## [3301] 12949 13047 13145 13243 13341 13439 13537 13635 13733 13831 13929 14027
## [3313] 14125 14223 14321 14419 14517 14615 14713 14811 14909 15007 15105 15203
## [3325] 15301 15399 15497 15595 15693 15791 15889 15987 16085 16183 16281 16379
## [3337] 16477 16575 16673 16771 16869 16967 17065 17163 17261 17359 17457 17555
## [3349] 17653 17751 17849 17947 18045 18143 18241 18339 18437 18535 18633 18731
## [3361] 18829 18927 19025 19123 19221 19319 19417 19515 19613 19711 19809 19907
## [3373] 20005 20103 20201 20299 20397 20495 20593 20691 20789 20887 20985 21083
## [3385] 21181 21279 21377 21475 21573 21671 21769 21867 21965 22063 22161 22259
## [3397] 22357 22455 22553 22651 22749 22847 22945 23043 23141 23239 23337 23435
## [3409] 23533 23631 23729 23827 23925 24023 24121 24219 24317 24415 24513 24611
## [3421] 24709 24807 24905 25003 25101 25199 25297 25395 25493 25591 25689 25787
## [3433] 14 112 210 308 406 504 602 700 798 896 994 1092
## [3445] 1190 1288 1386 1484 1582 1680 1778 1876 1974 2072 2170 2268
## [3457] 2366 2464 2562 2660 2758 2856 2954 3052 3150 3248 3346 3444
## [3469] 3542 3640 3738 3836 3934 4032 4130 4228 4326 4424 4522 4620
## [3481] 4718 4816 4914 5012 5110 5208 5306 5404 5502 5600 5698 5796
## [3493] 5894 5992 6090 6188 6286 6384 6482 6580 6678 6776 6874 6972
## [3505] 7070 7168 7266 7364 7462 7560 7658 7756 7854 7952 8050 8148
## [3517] 8246 8344 8442 8540 8638 8736 8834 8932 9030 9128 9226 9324
## [3529] 9422 9520 9618 9716 9814 9912 10010 10108 10206 10304 10402 10500
## [3541] 10598 10696 10794 10892 10990 11088 11186 11284 11382 11480 11578 11676
## [3553] 11774 11872 11970 12068 12166 12264 12362 12460 12558 12656 12754 12852
## [3565] 12950 13048 13146 13244 13342 13440 13538 13636 13734 13832 13930 14028
## [3577] 14126 14224 14322 14420 14518 14616 14714 14812 14910 15008 15106 15204
## [3589] 15302 15400 15498 15596 15694 15792 15890 15988 16086 16184 16282 16380
## [3601] 16478 16576 16674 16772 16870 16968 17066 17164 17262 17360 17458 17556
## [3613] 17654 17752 17850 17948 18046 18144 18242 18340 18438 18536 18634 18732
## [3625] 18830 18928 19026 19124 19222 19320 19418 19516 19614 19712 19810 19908
## [3637] 20006 20104 20202 20300 20398 20496 20594 20692 20790 20888 20986 21084
## [3649] 21182 21280 21378 21476 21574 21672 21770 21868 21966 22064 22162 22260
## [3661] 22358 22456 22554 22652 22750 22848 22946 23044 23142 23240 23338 23436
## [3673] 23534 23632 23730 23828 23926 24024 24122 24220 24318 24416 24514 24612
## [3685] 24710 24808 24906 25004 25102 25200 25298 25396 25494 25592 25690 25788
## [3697] 15 113 211 309 407 505 603 701 799 897 995 1093
## [3709] 1191 1289 1387 1485 1583 1681 1779 1877 1975 2073 2171 2269
## [3721] 2367 2465 2563 2661 2759 2857 2955 3053 3151 3249 3347 3445
## [3733] 3543 3641 3739 3837 3935 4033 4131 4229 4327 4425 4523 4621
## [3745] 4719 4817 4915 5013 5111 5209 5307 5405 5503 5601 5699 5797
## [3757] 5895 5993 6091 6189 6287 6385 6483 6581 6679 6777 6875 6973
## [3769] 7071 7169 7267 7365 7463 7561 7659 7757 7855 7953 8051 8149
## [3781] 8247 8345 8443 8541 8639 8737 8835 8933 9031 9129 9227 9325
## [3793] 9423 9521 9619 9717 9815 9913 10011 10109 10207 10305 10403 10501
## [3805] 10599 10697 10795 10893 10991 11089 11187 11285 11383 11481 11579 11677
## [3817] 11775 11873 11971 12069 12167 12265 12363 12461 12559 12657 12755 12853
## [3829] 12951 13049 13147 13245 13343 13441 13539 13637 13735 13833 13931 14029
## [3841] 14127 14225 14323 14421 14519 14617 14715 14813 14911 15009 15107 15205
## [3853] 15303 15401 15499 15597 15695 15793 15891 15989 16087 16185 16283 16381
## [3865] 16479 16577 16675 16773 16871 16969 17067 17165 17263 17361 17459 17557
## [3877] 17655 17753 17851 17949 18047 18145 18243 18341 18439 18537 18635 18733
## [3889] 18831 18929 19027 19125 19223 19321 19419 19517 19615 19713 19811 19909
## [3901] 20007 20105 20203 20301 20399 20497 20595 20693 20791 20889 20987 21085
## [3913] 21183 21281 21379 21477 21575 21673 21771 21869 21967 22065 22163 22261
## [3925] 22359 22457 22555 22653 22751 22849 22947 23045 23143 23241 23339 23437
## [3937] 23535 23633 23731 23829 23927 24025 24123 24221 24319 24417 24515 24613
## [3949] 24711 24809 24907 25005 25103 25201 25299 25397 25495 25593 25691 25789
## [3961] 16 114 212 310 408 506 604 702 800 898 996 1094
## [3973] 1192 1290 1388 1486 1584 1682 1780 1878 1976 2074 2172 2270
## [3985] 2368 2466 2564 2662 2760 2858 2956 3054 3152 3250 3348 3446
## [3997] 3544 3642 3740 3838 3936 4034 4132 4230 4328 4426 4524 4622
## [4009] 4720 4818 4916 5014 5112 5210 5308 5406 5504 5602 5700 5798
## [4021] 5896 5994 6092 6190 6288 6386 6484 6582 6680 6778 6876 6974
## [4033] 7072 7170 7268 7366 7464 7562 7660 7758 7856 7954 8052 8150
## [4045] 8248 8346 8444 8542 8640 8738 8836 8934 9032 9130 9228 9326
## [4057] 9424 9522 9620 9718 9816 9914 10012 10110 10208 10306 10404 10502
## [4069] 10600 10698 10796 10894 10992 11090 11188 11286 11384 11482 11580 11678
## [4081] 11776 11874 11972 12070 12168 12266 12364 12462 12560 12658 12756 12854
## [4093] 12952 13050 13148 13246 13344 13442 13540 13638 13736 13834 13932 14030
## [4105] 14128 14226 14324 14422 14520 14618 14716 14814 14912 15010 15108 15206
## [4117] 15304 15402 15500 15598 15696 15794 15892 15990 16088 16186 16284 16382
## [4129] 16480 16578 16676 16774 16872 16970 17068 17166 17264 17362 17460 17558
## [4141] 17656 17754 17852 17950 18048 18146 18244 18342 18440 18538 18636 18734
## [4153] 18832 18930 19028 19126 19224 19322 19420 19518 19616 19714 19812 19910
## [4165] 20008 20106 20204 20302 20400 20498 20596 20694 20792 20890 20988 21086
## [4177] 21184 21282 21380 21478 21576 21674 21772 21870 21968 22066 22164 22262
## [4189] 22360 22458 22556 22654 22752 22850 22948 23046 23144 23242 23340 23438
## [4201] 23536 23634 23732 23830 23928 24026 24124 24222 24320 24418 24516 24614
## [4213] 24712 24810 24908 25006 25104 25202 25300 25398 25496 25594 25692 25790
## [4225] 17 115 213 311 409 507 605 703 801 899 997 1095
## [4237] 1193 1291 1389 1487 1585 1683 1781 1879 1977 2075 2173 2271
## [4249] 2369 2467 2565 2663 2761 2859 2957 3055 3153 3251 3349 3447
## [4261] 3545 3643 3741 3839 3937 4035 4133 4231 4329 4427 4525 4623
## [4273] 4721 4819 4917 5015 5113 5211 5309 5407 5505 5603 5701 5799
## [4285] 5897 5995 6093 6191 6289 6387 6485 6583 6681 6779 6877 6975
## [4297] 7073 7171 7269 7367 7465 7563 7661 7759 7857 7955 8053 8151
## [4309] 8249 8347 8445 8543 8641 8739 8837 8935 9033 9131 9229 9327
## [4321] 9425 9523 9621 9719 9817 9915 10013 10111 10209 10307 10405 10503
## [4333] 10601 10699 10797 10895 10993 11091 11189 11287 11385 11483 11581 11679
## [4345] 11777 11875 11973 12071 12169 12267 12365 12463 12561 12659 12757 12855
## [4357] 12953 13051 13149 13247 13345 13443 13541 13639 13737 13835 13933 14031
## [4369] 14129 14227 14325 14423 14521 14619 14717 14815 14913 15011 15109 15207
## [4381] 15305 15403 15501 15599 15697 15795 15893 15991 16089 16187 16285 16383
## [4393] 16481 16579 16677 16775 16873 16971 17069 17167 17265 17363 17461 17559
## [4405] 17657 17755 17853 17951 18049 18147 18245 18343 18441 18539 18637 18735
## [4417] 18833 18931 19029 19127 19225 19323 19421 19519 19617 19715 19813 19911
## [4429] 20009 20107 20205 20303 20401 20499 20597 20695 20793 20891 20989 21087
## [4441] 21185 21283 21381 21479 21577 21675 21773 21871 21969 22067 22165 22263
## [4453] 22361 22459 22557 22655 22753 22851 22949 23047 23145 23243 23341 23439
## [4465] 23537 23635 23733 23831 23929 24027 24125 24223 24321 24419 24517 24615
## [4477] 24713 24811 24909 25007 25105 25203 25301 25399 25497 25595 25693 25791
## [4489] 18 116 214 312 410 508 606 704 802 900 998 1096
## [4501] 1194 1292 1390 1488 1586 1684 1782 1880 1978 2076 2174 2272
## [4513] 2370 2468 2566 2664 2762 2860 2958 3056 3154 3252 3350 3448
## [4525] 3546 3644 3742 3840 3938 4036 4134 4232 4330 4428 4526 4624
## [4537] 4722 4820 4918 5016 5114 5212 5310 5408 5506 5604 5702 5800
## [4549] 5898 5996 6094 6192 6290 6388 6486 6584 6682 6780 6878 6976
## [4561] 7074 7172 7270 7368 7466 7564 7662 7760 7858 7956 8054 8152
## [4573] 8250 8348 8446 8544 8642 8740 8838 8936 9034 9132 9230 9328
## [4585] 9426 9524 9622 9720 9818 9916 10014 10112 10210 10308 10406 10504
## [4597] 10602 10700 10798 10896 10994 11092 11190 11288 11386 11484 11582 11680
## [4609] 11778 11876 11974 12072 12170 12268 12366 12464 12562 12660 12758 12856
## [4621] 12954 13052 13150 13248 13346 13444 13542 13640 13738 13836 13934 14032
## [4633] 14130 14228 14326 14424 14522 14620 14718 14816 14914 15012 15110 15208
## [4645] 15306 15404 15502 15600 15698 15796 15894 15992 16090 16188 16286 16384
## [4657] 16482 16580 16678 16776 16874 16972 17070 17168 17266 17364 17462 17560
## [4669] 17658 17756 17854 17952 18050 18148 18246 18344 18442 18540 18638 18736
## [4681] 18834 18932 19030 19128 19226 19324 19422 19520 19618 19716 19814 19912
## [4693] 20010 20108 20206 20304 20402 20500 20598 20696 20794 20892 20990 21088
## [4705] 21186 21284 21382 21480 21578 21676 21774 21872 21970 22068 22166 22264
## [4717] 22362 22460 22558 22656 22754 22852 22950 23048 23146 23244 23342 23440
## [4729] 23538 23636 23734 23832 23930 24028 24126 24224 24322 24420 24518 24616
## [4741] 24714 24812 24910 25008 25106 25204 25302 25400 25498 25596 25694 25792
## [4753] 19 117 215 313 411 509 607 705 803 901 999 1097
## [4765] 1195 1293 1391 1489 1587 1685 1783 1881 1979 2077 2175 2273
## [4777] 2371 2469 2567 2665 2763 2861 2959 3057 3155 3253 3351 3449
## [4789] 3547 3645 3743 3841 3939 4037 4135 4233 4331 4429 4527 4625
## [4801] 4723 4821 4919 5017 5115 5213 5311 5409 5507 5605 5703 5801
## [4813] 5899 5997 6095 6193 6291 6389 6487 6585 6683 6781 6879 6977
## [4825] 7075 7173 7271 7369 7467 7565 7663 7761 7859 7957 8055 8153
## [4837] 8251 8349 8447 8545 8643 8741 8839 8937 9035 9133 9231 9329
## [4849] 9427 9525 9623 9721 9819 9917 10015 10113 10211 10309 10407 10505
## [4861] 10603 10701 10799 10897 10995 11093 11191 11289 11387 11485 11583 11681
## [4873] 11779 11877 11975 12073 12171 12269 12367 12465 12563 12661 12759 12857
## [4885] 12955 13053 13151 13249 13347 13445 13543 13641 13739 13837 13935 14033
## [4897] 14131 14229 14327 14425 14523 14621 14719 14817 14915 15013 15111 15209
## [4909] 15307 15405 15503 15601 15699 15797 15895 15993 16091 16189 16287 16385
## [4921] 16483 16581 16679 16777 16875 16973 17071 17169 17267 17365 17463 17561
## [4933] 17659 17757 17855 17953 18051 18149 18247 18345 18443 18541 18639 18737
## [4945] 18835 18933 19031 19129 19227 19325 19423 19521 19619 19717 19815 19913
## [4957] 20011 20109 20207 20305 20403 20501 20599 20697 20795 20893 20991 21089
## [4969] 21187 21285 21383 21481 21579 21677 21775 21873 21971 22069 22167 22265
## [4981] 22363 22461 22559 22657 22755 22853 22951 23049 23147 23245 23343 23441
## [4993] 23539 23637 23735 23833 23931 24029 24127 24225 24323 24421 24519 24617
## [5005] 24715 24813 24911 25009 25107 25205 25303 25401 25499 25597 25695 25793
## [5017] 20 118 216 314 412 510 608 706 804 902 1000 1098
## [5029] 1196 1294 1392 1490 1588 1686 1784 1882 1980 2078 2176 2274
## [5041] 2372 2470 2568 2666 2764 2862 2960 3058 3156 3254 3352 3450
## [5053] 3548 3646 3744 3842 3940 4038 4136 4234 4332 4430 4528 4626
## [5065] 4724 4822 4920 5018 5116 5214 5312 5410 5508 5606 5704 5802
## [5077] 5900 5998 6096 6194 6292 6390 6488 6586 6684 6782 6880 6978
## [5089] 7076 7174 7272 7370 7468 7566 7664 7762 7860 7958 8056 8154
## [5101] 8252 8350 8448 8546 8644 8742 8840 8938 9036 9134 9232 9330
## [5113] 9428 9526 9624 9722 9820 9918 10016 10114 10212 10310 10408 10506
## [5125] 10604 10702 10800 10898 10996 11094 11192 11290 11388 11486 11584 11682
## [5137] 11780 11878 11976 12074 12172 12270 12368 12466 12564 12662 12760 12858
## [5149] 12956 13054 13152 13250 13348 13446 13544 13642 13740 13838 13936 14034
## [5161] 14132 14230 14328 14426 14524 14622 14720 14818 14916 15014 15112 15210
## [5173] 15308 15406 15504 15602 15700 15798 15896 15994 16092 16190 16288 16386
## [5185] 16484 16582 16680 16778 16876 16974 17072 17170 17268 17366 17464 17562
## [5197] 17660 17758 17856 17954 18052 18150 18248 18346 18444 18542 18640 18738
## [5209] 18836 18934 19032 19130 19228 19326 19424 19522 19620 19718 19816 19914
## [5221] 20012 20110 20208 20306 20404 20502 20600 20698 20796 20894 20992 21090
## [5233] 21188 21286 21384 21482 21580 21678 21776 21874 21972 22070 22168 22266
## [5245] 22364 22462 22560 22658 22756 22854 22952 23050 23148 23246 23344 23442
## [5257] 23540 23638 23736 23834 23932 24030 24128 24226 24324 24422 24520 24618
## [5269] 24716 24814 24912 25010 25108 25206 25304 25402 25500 25598 25696 25794
## [5281] 21 119 217 315 413 511 609 707 805 903 1001 1099
## [5293] 1197 1295 1393 1491 1589 1687 1785 1883 1981 2079 2177 2275
## [5305] 2373 2471 2569 2667 2765 2863 2961 3059 3157 3255 3353 3451
## [5317] 3549 3647 3745 3843 3941 4039 4137 4235 4333 4431 4529 4627
## [5329] 4725 4823 4921 5019 5117 5215 5313 5411 5509 5607 5705 5803
## [5341] 5901 5999 6097 6195 6293 6391 6489 6587 6685 6783 6881 6979
## [5353] 7077 7175 7273 7371 7469 7567 7665 7763 7861 7959 8057 8155
## [5365] 8253 8351 8449 8547 8645 8743 8841 8939 9037 9135 9233 9331
## [5377] 9429 9527 9625 9723 9821 9919 10017 10115 10213 10311 10409 10507
## [5389] 10605 10703 10801 10899 10997 11095 11193 11291 11389 11487 11585 11683
## [5401] 11781 11879 11977 12075 12173 12271 12369 12467 12565 12663 12761 12859
## [5413] 12957 13055 13153 13251 13349 13447 13545 13643 13741 13839 13937 14035
## [5425] 14133 14231 14329 14427 14525 14623 14721 14819 14917 15015 15113 15211
## [5437] 15309 15407 15505 15603 15701 15799 15897 15995 16093 16191 16289 16387
## [5449] 16485 16583 16681 16779 16877 16975 17073 17171 17269 17367 17465 17563
## [5461] 17661 17759 17857 17955 18053 18151 18249 18347 18445 18543 18641 18739
## [5473] 18837 18935 19033 19131 19229 19327 19425 19523 19621 19719 19817 19915
## [5485] 20013 20111 20209 20307 20405 20503 20601 20699 20797 20895 20993 21091
## [5497] 21189 21287 21385 21483 21581 21679 21777 21875 21973 22071 22169 22267
## [5509] 22365 22463 22561 22659 22757 22855 22953 23051 23149 23247 23345 23443
## [5521] 23541 23639 23737 23835 23933 24031 24129 24227 24325 24423 24521 24619
## [5533] 24717 24815 24913 25011 25109 25207 25305 25403 25501 25599 25697 25795
## [5545] 22 120 218 316 414 512 610 708 806 904 1002 1100
## [5557] 1198 1296 1394 1492 1590 1688 1786 1884 1982 2080 2178 2276
## [5569] 2374 2472 2570 2668 2766 2864 2962 3060 3158 3256 3354 3452
## [5581] 3550 3648 3746 3844 3942 4040 4138 4236 4334 4432 4530 4628
## [5593] 4726 4824 4922 5020 5118 5216 5314 5412 5510 5608 5706 5804
## [5605] 5902 6000 6098 6196 6294 6392 6490 6588 6686 6784 6882 6980
## [5617] 7078 7176 7274 7372 7470 7568 7666 7764 7862 7960 8058 8156
## [5629] 8254 8352 8450 8548 8646 8744 8842 8940 9038 9136 9234 9332
## [5641] 9430 9528 9626 9724 9822 9920 10018 10116 10214 10312 10410 10508
## [5653] 10606 10704 10802 10900 10998 11096 11194 11292 11390 11488 11586 11684
## [5665] 11782 11880 11978 12076 12174 12272 12370 12468 12566 12664 12762 12860
## [5677] 12958 13056 13154 13252 13350 13448 13546 13644 13742 13840 13938 14036
## [5689] 14134 14232 14330 14428 14526 14624 14722 14820 14918 15016 15114 15212
## [5701] 15310 15408 15506 15604 15702 15800 15898 15996 16094 16192 16290 16388
## [5713] 16486 16584 16682 16780 16878 16976 17074 17172 17270 17368 17466 17564
## [5725] 17662 17760 17858 17956 18054 18152 18250 18348 18446 18544 18642 18740
## [5737] 18838 18936 19034 19132 19230 19328 19426 19524 19622 19720 19818 19916
## [5749] 20014 20112 20210 20308 20406 20504 20602 20700 20798 20896 20994 21092
## [5761] 21190 21288 21386 21484 21582 21680 21778 21876 21974 22072 22170 22268
## [5773] 22366 22464 22562 22660 22758 22856 22954 23052 23150 23248 23346 23444
## [5785] 23542 23640 23738 23836 23934 24032 24130 24228 24326 24424 24522 24620
## [5797] 24718 24816 24914 25012 25110 25208 25306 25404 25502 25600 25698 25796
## [5809] 23 121 219 317 415 513 611 709 807 905 1003 1101
## [5821] 1199 1297 1395 1493 1591 1689 1787 1885 1983 2081 2179 2277
## [5833] 2375 2473 2571 2669 2767 2865 2963 3061 3159 3257 3355 3453
## [5845] 3551 3649 3747 3845 3943 4041 4139 4237 4335 4433 4531 4629
## [5857] 4727 4825 4923 5021 5119 5217 5315 5413 5511 5609 5707 5805
## [5869] 5903 6001 6099 6197 6295 6393 6491 6589 6687 6785 6883 6981
## [5881] 7079 7177 7275 7373 7471 7569 7667 7765 7863 7961 8059 8157
## [5893] 8255 8353 8451 8549 8647 8745 8843 8941 9039 9137 9235 9333
## [5905] 9431 9529 9627 9725 9823 9921 10019 10117 10215 10313 10411 10509
## [5917] 10607 10705 10803 10901 10999 11097 11195 11293 11391 11489 11587 11685
## [5929] 11783 11881 11979 12077 12175 12273 12371 12469 12567 12665 12763 12861
## [5941] 12959 13057 13155 13253 13351 13449 13547 13645 13743 13841 13939 14037
## [5953] 14135 14233 14331 14429 14527 14625 14723 14821 14919 15017 15115 15213
## [5965] 15311 15409 15507 15605 15703 15801 15899 15997 16095 16193 16291 16389
## [5977] 16487 16585 16683 16781 16879 16977 17075 17173 17271 17369 17467 17565
## [5989] 17663 17761 17859 17957 18055 18153 18251 18349 18447 18545 18643 18741
## [6001] 18839 18937 19035 19133 19231 19329 19427 19525 19623 19721 19819 19917
## [6013] 20015 20113 20211 20309 20407 20505 20603 20701 20799 20897 20995 21093
## [6025] 21191 21289 21387 21485 21583 21681 21779 21877 21975 22073 22171 22269
## [6037] 22367 22465 22563 22661 22759 22857 22955 23053 23151 23249 23347 23445
## [6049] 23543 23641 23739 23837 23935 24033 24131 24229 24327 24425 24523 24621
## [6061] 24719 24817 24915 25013 25111 25209 25307 25405 25503 25601 25699 25797
## [6073] 24 122 220 318 416 514 612 710 808 906 1004 1102
## [6085] 1200 1298 1396 1494 1592 1690 1788 1886 1984 2082 2180 2278
## [6097] 2376 2474 2572 2670 2768 2866 2964 3062 3160 3258 3356 3454
## [6109] 3552 3650 3748 3846 3944 4042 4140 4238 4336 4434 4532 4630
## [6121] 4728 4826 4924 5022 5120 5218 5316 5414 5512 5610 5708 5806
## [6133] 5904 6002 6100 6198 6296 6394 6492 6590 6688 6786 6884 6982
## [6145] 7080 7178 7276 7374 7472 7570 7668 7766 7864 7962 8060 8158
## [6157] 8256 8354 8452 8550 8648 8746 8844 8942 9040 9138 9236 9334
## [6169] 9432 9530 9628 9726 9824 9922 10020 10118 10216 10314 10412 10510
## [6181] 10608 10706 10804 10902 11000 11098 11196 11294 11392 11490 11588 11686
## [6193] 11784 11882 11980 12078 12176 12274 12372 12470 12568 12666 12764 12862
## [6205] 12960 13058 13156 13254 13352 13450 13548 13646 13744 13842 13940 14038
## [6217] 14136 14234 14332 14430 14528 14626 14724 14822 14920 15018 15116 15214
## [6229] 15312 15410 15508 15606 15704 15802 15900 15998 16096 16194 16292 16390
## [6241] 16488 16586 16684 16782 16880 16978 17076 17174 17272 17370 17468 17566
## [6253] 17664 17762 17860 17958 18056 18154 18252 18350 18448 18546 18644 18742
## [6265] 18840 18938 19036 19134 19232 19330 19428 19526 19624 19722 19820 19918
## [6277] 20016 20114 20212 20310 20408 20506 20604 20702 20800 20898 20996 21094
## [6289] 21192 21290 21388 21486 21584 21682 21780 21878 21976 22074 22172 22270
## [6301] 22368 22466 22564 22662 22760 22858 22956 23054 23152 23250 23348 23446
## [6313] 23544 23642 23740 23838 23936 24034 24132 24230 24328 24426 24524 24622
## [6325] 24720 24818 24916 25014 25112 25210 25308 25406 25504 25602 25700 25798
## [6337] 25 123 221 319 417 515 613 711 809 907 1005 1103
## [6349] 1201 1299 1397 1495 1593 1691 1789 1887 1985 2083 2181 2279
## [6361] 2377 2475 2573 2671 2769 2867 2965 3063 3161 3259 3357 3455
## [6373] 3553 3651 3749 3847 3945 4043 4141 4239 4337 4435 4533 4631
## [6385] 4729 4827 4925 5023 5121 5219 5317 5415 5513 5611 5709 5807
## [6397] 5905 6003 6101 6199 6297 6395 6493 6591 6689 6787 6885 6983
## [6409] 7081 7179 7277 7375 7473 7571 7669 7767 7865 7963 8061 8159
## [6421] 8257 8355 8453 8551 8649 8747 8845 8943 9041 9139 9237 9335
## [6433] 9433 9531 9629 9727 9825 9923 10021 10119 10217 10315 10413 10511
## [6445] 10609 10707 10805 10903 11001 11099 11197 11295 11393 11491 11589 11687
## [6457] 11785 11883 11981 12079 12177 12275 12373 12471 12569 12667 12765 12863
## [6469] 12961 13059 13157 13255 13353 13451 13549 13647 13745 13843 13941 14039
## [6481] 14137 14235 14333 14431 14529 14627 14725 14823 14921 15019 15117 15215
## [6493] 15313 15411 15509 15607 15705 15803 15901 15999 16097 16195 16293 16391
## [6505] 16489 16587 16685 16783 16881 16979 17077 17175 17273 17371 17469 17567
## [6517] 17665 17763 17861 17959 18057 18155 18253 18351 18449 18547 18645 18743
## [6529] 18841 18939 19037 19135 19233 19331 19429 19527 19625 19723 19821 19919
## [6541] 20017 20115 20213 20311 20409 20507 20605 20703 20801 20899 20997 21095
## [6553] 21193 21291 21389 21487 21585 21683 21781 21879 21977 22075 22173 22271
## [6565] 22369 22467 22565 22663 22761 22859 22957 23055 23153 23251 23349 23447
## [6577] 23545 23643 23741 23839 23937 24035 24133 24231 24329 24427 24525 24623
## [6589] 24721 24819 24917 25015 25113 25211 25309 25407 25505 25603 25701 25799
## [6601] 26 124 222 320 418 516 614 712 810 908 1006 1104
## [6613] 1202 1300 1398 1496 1594 1692 1790 1888 1986 2084 2182 2280
## [6625] 2378 2476 2574 2672 2770 2868 2966 3064 3162 3260 3358 3456
## [6637] 3554 3652 3750 3848 3946 4044 4142 4240 4338 4436 4534 4632
## [6649] 4730 4828 4926 5024 5122 5220 5318 5416 5514 5612 5710 5808
## [6661] 5906 6004 6102 6200 6298 6396 6494 6592 6690 6788 6886 6984
## [6673] 7082 7180 7278 7376 7474 7572 7670 7768 7866 7964 8062 8160
## [6685] 8258 8356 8454 8552 8650 8748 8846 8944 9042 9140 9238 9336
## [6697] 9434 9532 9630 9728 9826 9924 10022 10120 10218 10316 10414 10512
## [6709] 10610 10708 10806 10904 11002 11100 11198 11296 11394 11492 11590 11688
## [6721] 11786 11884 11982 12080 12178 12276 12374 12472 12570 12668 12766 12864
## [6733] 12962 13060 13158 13256 13354 13452 13550 13648 13746 13844 13942 14040
## [6745] 14138 14236 14334 14432 14530 14628 14726 14824 14922 15020 15118 15216
## [6757] 15314 15412 15510 15608 15706 15804 15902 16000 16098 16196 16294 16392
## [6769] 16490 16588 16686 16784 16882 16980 17078 17176 17274 17372 17470 17568
## [6781] 17666 17764 17862 17960 18058 18156 18254 18352 18450 18548 18646 18744
## [6793] 18842 18940 19038 19136 19234 19332 19430 19528 19626 19724 19822 19920
## [6805] 20018 20116 20214 20312 20410 20508 20606 20704 20802 20900 20998 21096
## [6817] 21194 21292 21390 21488 21586 21684 21782 21880 21978 22076 22174 22272
## [6829] 22370 22468 22566 22664 22762 22860 22958 23056 23154 23252 23350 23448
## [6841] 23546 23644 23742 23840 23938 24036 24134 24232 24330 24428 24526 24624
## [6853] 24722 24820 24918 25016 25114 25212 25310 25408 25506 25604 25702 25800
## [6865] 27 125 223 321 419 517 615 713 811 909 1007 1105
## [6877] 1203 1301 1399 1497 1595 1693 1791 1889 1987 2085 2183 2281
## [6889] 2379 2477 2575 2673 2771 2869 2967 3065 3163 3261 3359 3457
## [6901] 3555 3653 3751 3849 3947 4045 4143 4241 4339 4437 4535 4633
## [6913] 4731 4829 4927 5025 5123 5221 5319 5417 5515 5613 5711 5809
## [6925] 5907 6005 6103 6201 6299 6397 6495 6593 6691 6789 6887 6985
## [6937] 7083 7181 7279 7377 7475 7573 7671 7769 7867 7965 8063 8161
## [6949] 8259 8357 8455 8553 8651 8749 8847 8945 9043 9141 9239 9337
## [6961] 9435 9533 9631 9729 9827 9925 10023 10121 10219 10317 10415 10513
## [6973] 10611 10709 10807 10905 11003 11101 11199 11297 11395 11493 11591 11689
## [6985] 11787 11885 11983 12081 12179 12277 12375 12473 12571 12669 12767 12865
## [6997] 12963 13061 13159 13257 13355 13453 13551 13649 13747 13845 13943 14041
## [7009] 14139 14237 14335 14433 14531 14629 14727 14825 14923 15021 15119 15217
## [7021] 15315 15413 15511 15609 15707 15805 15903 16001 16099 16197 16295 16393
## [7033] 16491 16589 16687 16785 16883 16981 17079 17177 17275 17373 17471 17569
## [7045] 17667 17765 17863 17961 18059 18157 18255 18353 18451 18549 18647 18745
## [7057] 18843 18941 19039 19137 19235 19333 19431 19529 19627 19725 19823 19921
## [7069] 20019 20117 20215 20313 20411 20509 20607 20705 20803 20901 20999 21097
## [7081] 21195 21293 21391 21489 21587 21685 21783 21881 21979 22077 22175 22273
## [7093] 22371 22469 22567 22665 22763 22861 22959 23057 23155 23253 23351 23449
## [7105] 23547 23645 23743 23841 23939 24037 24135 24233 24331 24429 24527 24625
## [7117] 24723 24821 24919 25017 25115 25213 25311 25409 25507 25605 25703 25801
## [7129] 28 126 224 322 420 518 616 714 812 910 1008 1106
## [7141] 1204 1302 1400 1498 1596 1694 1792 1890 1988 2086 2184 2282
## [7153] 2380 2478 2576 2674 2772 2870 2968 3066 3164 3262 3360 3458
## [7165] 3556 3654 3752 3850 3948 4046 4144 4242 4340 4438 4536 4634
## [7177] 4732 4830 4928 5026 5124 5222 5320 5418 5516 5614 5712 5810
## [7189] 5908 6006 6104 6202 6300 6398 6496 6594 6692 6790 6888 6986
## [7201] 7084 7182 7280 7378 7476 7574 7672 7770 7868 7966 8064 8162
## [7213] 8260 8358 8456 8554 8652 8750 8848 8946 9044 9142 9240 9338
## [7225] 9436 9534 9632 9730 9828 9926 10024 10122 10220 10318 10416 10514
## [7237] 10612 10710 10808 10906 11004 11102 11200 11298 11396 11494 11592 11690
## [7249] 11788 11886 11984 12082 12180 12278 12376 12474 12572 12670 12768 12866
## [7261] 12964 13062 13160 13258 13356 13454 13552 13650 13748 13846 13944 14042
## [7273] 14140 14238 14336 14434 14532 14630 14728 14826 14924 15022 15120 15218
## [7285] 15316 15414 15512 15610 15708 15806 15904 16002 16100 16198 16296 16394
## [7297] 16492 16590 16688 16786 16884 16982 17080 17178 17276 17374 17472 17570
## [7309] 17668 17766 17864 17962 18060 18158 18256 18354 18452 18550 18648 18746
## [7321] 18844 18942 19040 19138 19236 19334 19432 19530 19628 19726 19824 19922
## [7333] 20020 20118 20216 20314 20412 20510 20608 20706 20804 20902 21000 21098
## [7345] 21196 21294 21392 21490 21588 21686 21784 21882 21980 22078 22176 22274
## [7357] 22372 22470 22568 22666 22764 22862 22960 23058 23156 23254 23352 23450
## [7369] 23548 23646 23744 23842 23940 24038 24136 24234 24332 24430 24528 24626
## [7381] 24724 24822 24920 25018 25116 25214 25312 25410 25508 25606 25704 25802
## [7393] 29 127 225 323 421 519 617 715 813 911 1009 1107
## [7405] 1205 1303 1401 1499 1597 1695 1793 1891 1989 2087 2185 2283
## [7417] 2381 2479 2577 2675 2773 2871 2969 3067 3165 3263 3361 3459
## [7429] 3557 3655 3753 3851 3949 4047 4145 4243 4341 4439 4537 4635
## [7441] 4733 4831 4929 5027 5125 5223 5321 5419 5517 5615 5713 5811
## [7453] 5909 6007 6105 6203 6301 6399 6497 6595 6693 6791 6889 6987
## [7465] 7085 7183 7281 7379 7477 7575 7673 7771 7869 7967 8065 8163
## [7477] 8261 8359 8457 8555 8653 8751 8849 8947 9045 9143 9241 9339
## [7489] 9437 9535 9633 9731 9829 9927 10025 10123 10221 10319 10417 10515
## [7501] 10613 10711 10809 10907 11005 11103 11201 11299 11397 11495 11593 11691
## [7513] 11789 11887 11985 12083 12181 12279 12377 12475 12573 12671 12769 12867
## [7525] 12965 13063 13161 13259 13357 13455 13553 13651 13749 13847 13945 14043
## [7537] 14141 14239 14337 14435 14533 14631 14729 14827 14925 15023 15121 15219
## [7549] 15317 15415 15513 15611 15709 15807 15905 16003 16101 16199 16297 16395
## [7561] 16493 16591 16689 16787 16885 16983 17081 17179 17277 17375 17473 17571
## [7573] 17669 17767 17865 17963 18061 18159 18257 18355 18453 18551 18649 18747
## [7585] 18845 18943 19041 19139 19237 19335 19433 19531 19629 19727 19825 19923
## [7597] 20021 20119 20217 20315 20413 20511 20609 20707 20805 20903 21001 21099
## [7609] 21197 21295 21393 21491 21589 21687 21785 21883 21981 22079 22177 22275
## [7621] 22373 22471 22569 22667 22765 22863 22961 23059 23157 23255 23353 23451
## [7633] 23549 23647 23745 23843 23941 24039 24137 24235 24333 24431 24529 24627
## [7645] 24725 24823 24921 25019 25117 25215 25313 25411 25509 25607 25705 25803
## [7657] 30 128 226 324 422 520 618 716 814 912 1010 1108
## [7669] 1206 1304 1402 1500 1598 1696 1794 1892 1990 2088 2186 2284
## [7681] 2382 2480 2578 2676 2774 2872 2970 3068 3166 3264 3362 3460
## [7693] 3558 3656 3754 3852 3950 4048 4146 4244 4342 4440 4538 4636
## [7705] 4734 4832 4930 5028 5126 5224 5322 5420 5518 5616 5714 5812
## [7717] 5910 6008 6106 6204 6302 6400 6498 6596 6694 6792 6890 6988
## [7729] 7086 7184 7282 7380 7478 7576 7674 7772 7870 7968 8066 8164
## [7741] 8262 8360 8458 8556 8654 8752 8850 8948 9046 9144 9242 9340
## [7753] 9438 9536 9634 9732 9830 9928 10026 10124 10222 10320 10418 10516
## [7765] 10614 10712 10810 10908 11006 11104 11202 11300 11398 11496 11594 11692
## [7777] 11790 11888 11986 12084 12182 12280 12378 12476 12574 12672 12770 12868
## [7789] 12966 13064 13162 13260 13358 13456 13554 13652 13750 13848 13946 14044
## [7801] 14142 14240 14338 14436 14534 14632 14730 14828 14926 15024 15122 15220
## [7813] 15318 15416 15514 15612 15710 15808 15906 16004 16102 16200 16298 16396
## [7825] 16494 16592 16690 16788 16886 16984 17082 17180 17278 17376 17474 17572
## [7837] 17670 17768 17866 17964 18062 18160 18258 18356 18454 18552 18650 18748
## [7849] 18846 18944 19042 19140 19238 19336 19434 19532 19630 19728 19826 19924
## [7861] 20022 20120 20218 20316 20414 20512 20610 20708 20806 20904 21002 21100
## [7873] 21198 21296 21394 21492 21590 21688 21786 21884 21982 22080 22178 22276
## [7885] 22374 22472 22570 22668 22766 22864 22962 23060 23158 23256 23354 23452
## [7897] 23550 23648 23746 23844 23942 24040 24138 24236 24334 24432 24530 24628
## [7909] 24726 24824 24922 25020 25118 25216 25314 25412 25510 25608 25706 25804
## [7921] 31 129 227 325 423 521 619 717 815 913 1011 1109
## [7933] 1207 1305 1403 1501 1599 1697 1795 1893 1991 2089 2187 2285
## [7945] 2383 2481 2579 2677 2775 2873 2971 3069 3167 3265 3363 3461
## [7957] 3559 3657 3755 3853 3951 4049 4147 4245 4343 4441 4539 4637
## [7969] 4735 4833 4931 5029 5127 5225 5323 5421 5519 5617 5715 5813
## [7981] 5911 6009 6107 6205 6303 6401 6499 6597 6695 6793 6891 6989
## [7993] 7087 7185 7283 7381 7479 7577 7675 7773 7871 7969 8067 8165
## [8005] 8263 8361 8459 8557 8655 8753 8851 8949 9047 9145 9243 9341
## [8017] 9439 9537 9635 9733 9831 9929 10027 10125 10223 10321 10419 10517
## [8029] 10615 10713 10811 10909 11007 11105 11203 11301 11399 11497 11595 11693
## [8041] 11791 11889 11987 12085 12183 12281 12379 12477 12575 12673 12771 12869
## [8053] 12967 13065 13163 13261 13359 13457 13555 13653 13751 13849 13947 14045
## [8065] 14143 14241 14339 14437 14535 14633 14731 14829 14927 15025 15123 15221
## [8077] 15319 15417 15515 15613 15711 15809 15907 16005 16103 16201 16299 16397
## [8089] 16495 16593 16691 16789 16887 16985 17083 17181 17279 17377 17475 17573
## [8101] 17671 17769 17867 17965 18063 18161 18259 18357 18455 18553 18651 18749
## [8113] 18847 18945 19043 19141 19239 19337 19435 19533 19631 19729 19827 19925
## [8125] 20023 20121 20219 20317 20415 20513 20611 20709 20807 20905 21003 21101
## [8137] 21199 21297 21395 21493 21591 21689 21787 21885 21983 22081 22179 22277
## [8149] 22375 22473 22571 22669 22767 22865 22963 23061 23159 23257 23355 23453
## [8161] 23551 23649 23747 23845 23943 24041 24139 24237 24335 24433 24531 24629
## [8173] 24727 24825 24923 25021 25119 25217 25315 25413 25511 25609 25707 25805
## [8185] 32 130 228 326 424 522 620 718 816 914 1012 1110
## [8197] 1208 1306 1404 1502 1600 1698 1796 1894 1992 2090 2188 2286
## [8209] 2384 2482 2580 2678 2776 2874 2972 3070 3168 3266 3364 3462
## [8221] 3560 3658 3756 3854 3952 4050 4148 4246 4344 4442 4540 4638
## [8233] 4736 4834 4932 5030 5128 5226 5324 5422 5520 5618 5716 5814
## [8245] 5912 6010 6108 6206 6304 6402 6500 6598 6696 6794 6892 6990
## [8257] 7088 7186 7284 7382 7480 7578 7676 7774 7872 7970 8068 8166
## [8269] 8264 8362 8460 8558 8656 8754 8852 8950 9048 9146 9244 9342
## [8281] 9440 9538 9636 9734 9832 9930 10028 10126 10224 10322 10420 10518
## [8293] 10616 10714 10812 10910 11008 11106 11204 11302 11400 11498 11596 11694
## [8305] 11792 11890 11988 12086 12184 12282 12380 12478 12576 12674 12772 12870
## [8317] 12968 13066 13164 13262 13360 13458 13556 13654 13752 13850 13948 14046
## [8329] 14144 14242 14340 14438 14536 14634 14732 14830 14928 15026 15124 15222
## [8341] 15320 15418 15516 15614 15712 15810 15908 16006 16104 16202 16300 16398
## [8353] 16496 16594 16692 16790 16888 16986 17084 17182 17280 17378 17476 17574
## [8365] 17672 17770 17868 17966 18064 18162 18260 18358 18456 18554 18652 18750
## [8377] 18848 18946 19044 19142 19240 19338 19436 19534 19632 19730 19828 19926
## [8389] 20024 20122 20220 20318 20416 20514 20612 20710 20808 20906 21004 21102
## [8401] 21200 21298 21396 21494 21592 21690 21788 21886 21984 22082 22180 22278
## [8413] 22376 22474 22572 22670 22768 22866 22964 23062 23160 23258 23356 23454
## [8425] 23552 23650 23748 23846 23944 24042 24140 24238 24336 24434 24532 24630
## [8437] 24728 24826 24924 25022 25120 25218 25316 25414 25512 25610 25708 25806
## [8449] 33 131 229 327 425 523 621 719 817 915 1013 1111
## [8461] 1209 1307 1405 1503 1601 1699 1797 1895 1993 2091 2189 2287
## [8473] 2385 2483 2581 2679 2777 2875 2973 3071 3169 3267 3365 3463
## [8485] 3561 3659 3757 3855 3953 4051 4149 4247 4345 4443 4541 4639
## [8497] 4737 4835 4933 5031 5129 5227 5325 5423 5521 5619 5717 5815
## [8509] 5913 6011 6109 6207 6305 6403 6501 6599 6697 6795 6893 6991
## [8521] 7089 7187 7285 7383 7481 7579 7677 7775 7873 7971 8069 8167
## [8533] 8265 8363 8461 8559 8657 8755 8853 8951 9049 9147 9245 9343
## [8545] 9441 9539 9637 9735 9833 9931 10029 10127 10225 10323 10421 10519
## [8557] 10617 10715 10813 10911 11009 11107 11205 11303 11401 11499 11597 11695
## [8569] 11793 11891 11989 12087 12185 12283 12381 12479 12577 12675 12773 12871
## [8581] 12969 13067 13165 13263 13361 13459 13557 13655 13753 13851 13949 14047
## [8593] 14145 14243 14341 14439 14537 14635 14733 14831 14929 15027 15125 15223
## [8605] 15321 15419 15517 15615 15713 15811 15909 16007 16105 16203 16301 16399
## [8617] 16497 16595 16693 16791 16889 16987 17085 17183 17281 17379 17477 17575
## [8629] 17673 17771 17869 17967 18065 18163 18261 18359 18457 18555 18653 18751
## [8641] 18849 18947 19045 19143 19241 19339 19437 19535 19633 19731 19829 19927
## [8653] 20025 20123 20221 20319 20417 20515 20613 20711 20809 20907 21005 21103
## [8665] 21201 21299 21397 21495 21593 21691 21789 21887 21985 22083 22181 22279
## [8677] 22377 22475 22573 22671 22769 22867 22965 23063 23161 23259 23357 23455
## [8689] 23553 23651 23749 23847 23945 24043 24141 24239 24337 24435 24533 24631
## [8701] 24729 24827 24925 25023 25121 25219 25317 25415 25513 25611 25709 25807
## [8713] 34 132 230 328 426 524 622 720 818 916 1014 1112
## [8725] 1210 1308 1406 1504 1602 1700 1798 1896 1994 2092 2190 2288
## [8737] 2386 2484 2582 2680 2778 2876 2974 3072 3170 3268 3366 3464
## [8749] 3562 3660 3758 3856 3954 4052 4150 4248 4346 4444 4542 4640
## [8761] 4738 4836 4934 5032 5130 5228 5326 5424 5522 5620 5718 5816
## [8773] 5914 6012 6110 6208 6306 6404 6502 6600 6698 6796 6894 6992
## [8785] 7090 7188 7286 7384 7482 7580 7678 7776 7874 7972 8070 8168
## [8797] 8266 8364 8462 8560 8658 8756 8854 8952 9050 9148 9246 9344
## [8809] 9442 9540 9638 9736 9834 9932 10030 10128 10226 10324 10422 10520
## [8821] 10618 10716 10814 10912 11010 11108 11206 11304 11402 11500 11598 11696
## [8833] 11794 11892 11990 12088 12186 12284 12382 12480 12578 12676 12774 12872
## [8845] 12970 13068 13166 13264 13362 13460 13558 13656 13754 13852 13950 14048
## [8857] 14146 14244 14342 14440 14538 14636 14734 14832 14930 15028 15126 15224
## [8869] 15322 15420 15518 15616 15714 15812 15910 16008 16106 16204 16302 16400
## [8881] 16498 16596 16694 16792 16890 16988 17086 17184 17282 17380 17478 17576
## [8893] 17674 17772 17870 17968 18066 18164 18262 18360 18458 18556 18654 18752
## [8905] 18850 18948 19046 19144 19242 19340 19438 19536 19634 19732 19830 19928
## [8917] 20026 20124 20222 20320 20418 20516 20614 20712 20810 20908 21006 21104
## [8929] 21202 21300 21398 21496 21594 21692 21790 21888 21986 22084 22182 22280
## [8941] 22378 22476 22574 22672 22770 22868 22966 23064 23162 23260 23358 23456
## [8953] 23554 23652 23750 23848 23946 24044 24142 24240 24338 24436 24534 24632
## [8965] 24730 24828 24926 25024 25122 25220 25318 25416 25514 25612 25710 25808
## [8977] 35 133 231 329 427 525 623 721 819 917 1015 1113
## [8989] 1211 1309 1407 1505 1603 1701 1799 1897 1995 2093 2191 2289
## [9001] 2387 2485 2583 2681 2779 2877 2975 3073 3171 3269 3367 3465
## [9013] 3563 3661 3759 3857 3955 4053 4151 4249 4347 4445 4543 4641
## [9025] 4739 4837 4935 5033 5131 5229 5327 5425 5523 5621 5719 5817
## [9037] 5915 6013 6111 6209 6307 6405 6503 6601 6699 6797 6895 6993
## [9049] 7091 7189 7287 7385 7483 7581 7679 7777 7875 7973 8071 8169
## [9061] 8267 8365 8463 8561 8659 8757 8855 8953 9051 9149 9247 9345
## [9073] 9443 9541 9639 9737 9835 9933 10031 10129 10227 10325 10423 10521
## [9085] 10619 10717 10815 10913 11011 11109 11207 11305 11403 11501 11599 11697
## [9097] 11795 11893 11991 12089 12187 12285 12383 12481 12579 12677 12775 12873
## [9109] 12971 13069 13167 13265 13363 13461 13559 13657 13755 13853 13951 14049
## [9121] 14147 14245 14343 14441 14539 14637 14735 14833 14931 15029 15127 15225
## [9133] 15323 15421 15519 15617 15715 15813 15911 16009 16107 16205 16303 16401
## [9145] 16499 16597 16695 16793 16891 16989 17087 17185 17283 17381 17479 17577
## [9157] 17675 17773 17871 17969 18067 18165 18263 18361 18459 18557 18655 18753
## [9169] 18851 18949 19047 19145 19243 19341 19439 19537 19635 19733 19831 19929
## [9181] 20027 20125 20223 20321 20419 20517 20615 20713 20811 20909 21007 21105
## [9193] 21203 21301 21399 21497 21595 21693 21791 21889 21987 22085 22183 22281
## [9205] 22379 22477 22575 22673 22771 22869 22967 23065 23163 23261 23359 23457
## [9217] 23555 23653 23751 23849 23947 24045 24143 24241 24339 24437 24535 24633
## [9229] 24731 24829 24927 25025 25123 25221 25319 25417 25515 25613 25711 25809
## [9241] 36 134 232 330 428 526 624 722 820 918 1016 1114
## [9253] 1212 1310 1408 1506 1604 1702 1800 1898 1996 2094 2192 2290
## [9265] 2388 2486 2584 2682 2780 2878 2976 3074 3172 3270 3368 3466
## [9277] 3564 3662 3760 3858 3956 4054 4152 4250 4348 4446 4544 4642
## [9289] 4740 4838 4936 5034 5132 5230 5328 5426 5524 5622 5720 5818
## [9301] 5916 6014 6112 6210 6308 6406 6504 6602 6700 6798 6896 6994
## [9313] 7092 7190 7288 7386 7484 7582 7680 7778 7876 7974 8072 8170
## [9325] 8268 8366 8464 8562 8660 8758 8856 8954 9052 9150 9248 9346
## [9337] 9444 9542 9640 9738 9836 9934 10032 10130 10228 10326 10424 10522
## [9349] 10620 10718 10816 10914 11012 11110 11208 11306 11404 11502 11600 11698
## [9361] 11796 11894 11992 12090 12188 12286 12384 12482 12580 12678 12776 12874
## [9373] 12972 13070 13168 13266 13364 13462 13560 13658 13756 13854 13952 14050
## [9385] 14148 14246 14344 14442 14540 14638 14736 14834 14932 15030 15128 15226
## [9397] 15324 15422 15520 15618 15716 15814 15912 16010 16108 16206 16304 16402
## [9409] 16500 16598 16696 16794 16892 16990 17088 17186 17284 17382 17480 17578
## [9421] 17676 17774 17872 17970 18068 18166 18264 18362 18460 18558 18656 18754
## [9433] 18852 18950 19048 19146 19244 19342 19440 19538 19636 19734 19832 19930
## [9445] 20028 20126 20224 20322 20420 20518 20616 20714 20812 20910 21008 21106
## [9457] 21204 21302 21400 21498 21596 21694 21792 21890 21988 22086 22184 22282
## [9469] 22380 22478 22576 22674 22772 22870 22968 23066 23164 23262 23360 23458
## [9481] 23556 23654 23752 23850 23948 24046 24144 24242 24340 24438 24536 24634
## [9493] 24732 24830 24928 25026 25124 25222 25320 25418 25516 25614 25712 25810
## [9505] 37 135 233 331 429 527 625 723 821 919 1017 1115
## [9517] 1213 1311 1409 1507 1605 1703 1801 1899 1997 2095 2193 2291
## [9529] 2389 2487 2585 2683 2781 2879 2977 3075 3173 3271 3369 3467
## [9541] 3565 3663 3761 3859 3957 4055 4153 4251 4349 4447 4545 4643
## [9553] 4741 4839 4937 5035 5133 5231 5329 5427 5525 5623 5721 5819
## [9565] 5917 6015 6113 6211 6309 6407 6505 6603 6701 6799 6897 6995
## [9577] 7093 7191 7289 7387 7485 7583 7681 7779 7877 7975 8073 8171
## [9589] 8269 8367 8465 8563 8661 8759 8857 8955 9053 9151 9249 9347
## [9601] 9445 9543 9641 9739 9837 9935 10033 10131 10229 10327 10425 10523
## [9613] 10621 10719 10817 10915 11013 11111 11209 11307 11405 11503 11601 11699
## [9625] 11797 11895 11993 12091 12189 12287 12385 12483 12581 12679 12777 12875
## [9637] 12973 13071 13169 13267 13365 13463 13561 13659 13757 13855 13953 14051
## [9649] 14149 14247 14345 14443 14541 14639 14737 14835 14933 15031 15129 15227
## [9661] 15325 15423 15521 15619 15717 15815 15913 16011 16109 16207 16305 16403
## [9673] 16501 16599 16697 16795 16893 16991 17089 17187 17285 17383 17481 17579
## [9685] 17677 17775 17873 17971 18069 18167 18265 18363 18461 18559 18657 18755
## [9697] 18853 18951 19049 19147 19245 19343 19441 19539 19637 19735 19833 19931
## [9709] 20029 20127 20225 20323 20421 20519 20617 20715 20813 20911 21009 21107
## [9721] 21205 21303 21401 21499 21597 21695 21793 21891 21989 22087 22185 22283
## [9733] 22381 22479 22577 22675 22773 22871 22969 23067 23165 23263 23361 23459
## [9745] 23557 23655 23753 23851 23949 24047 24145 24243 24341 24439 24537 24635
## [9757] 24733 24831 24929 25027 25125 25223 25321 25419 25517 25615 25713 25811
## [9769] 38 136 234 332 430 528 626 724 822 920 1018 1116
## [9781] 1214 1312 1410 1508 1606 1704 1802 1900 1998 2096 2194 2292
## [9793] 2390 2488 2586 2684 2782 2880 2978 3076 3174 3272 3370 3468
## [9805] 3566 3664 3762 3860 3958 4056 4154 4252 4350 4448 4546 4644
## [9817] 4742 4840 4938 5036 5134 5232 5330 5428 5526 5624 5722 5820
## [9829] 5918 6016 6114 6212 6310 6408 6506 6604 6702 6800 6898 6996
## [9841] 7094 7192 7290 7388 7486 7584 7682 7780 7878 7976 8074 8172
## [9853] 8270 8368 8466 8564 8662 8760 8858 8956 9054 9152 9250 9348
## [9865] 9446 9544 9642 9740 9838 9936 10034 10132 10230 10328 10426 10524
## [9877] 10622 10720 10818 10916 11014 11112 11210 11308 11406 11504 11602 11700
## [9889] 11798 11896 11994 12092 12190 12288 12386 12484 12582 12680 12778 12876
## [9901] 12974 13072 13170 13268 13366 13464 13562 13660 13758 13856 13954 14052
## [9913] 14150 14248 14346 14444 14542 14640 14738 14836 14934 15032 15130 15228
## [9925] 15326 15424 15522 15620 15718 15816 15914 16012 16110 16208 16306 16404
## [9937] 16502 16600 16698 16796 16894 16992 17090 17188 17286 17384 17482 17580
## [9949] 17678 17776 17874 17972 18070 18168 18266 18364 18462 18560 18658 18756
## [9961] 18854 18952 19050 19148 19246 19344 19442 19540 19638 19736 19834 19932
## [9973] 20030 20128 20226 20324 20422 20520 20618 20716 20814 20912 21010 21108
## [9985] 21206 21304 21402 21500 21598 21696 21794 21892 21990 22088 22186 22284
## [9997] 22382 22480 22578 22676 22774 22872 22970 23068 23166 23264 23362 23460
## [10009] 23558 23656 23754 23852 23950 24048 24146 24244 24342 24440 24538 24636
## [10021] 24734 24832 24930 25028 25126 25224 25322 25420 25518 25616 25714 25812
## [10033] 39 137 235 333 431 529 627 725 823 921 1019 1117
## [10045] 1215 1313 1411 1509 1607 1705 1803 1901 1999 2097 2195 2293
## [10057] 2391 2489 2587 2685 2783 2881 2979 3077 3175 3273 3371 3469
## [10069] 3567 3665 3763 3861 3959 4057 4155 4253 4351 4449 4547 4645
## [10081] 4743 4841 4939 5037 5135 5233 5331 5429 5527 5625 5723 5821
## [10093] 5919 6017 6115 6213 6311 6409 6507 6605 6703 6801 6899 6997
## [10105] 7095 7193 7291 7389 7487 7585 7683 7781 7879 7977 8075 8173
## [10117] 8271 8369 8467 8565 8663 8761 8859 8957 9055 9153 9251 9349
## [10129] 9447 9545 9643 9741 9839 9937 10035 10133 10231 10329 10427 10525
## [10141] 10623 10721 10819 10917 11015 11113 11211 11309 11407 11505 11603 11701
## [10153] 11799 11897 11995 12093 12191 12289 12387 12485 12583 12681 12779 12877
## [10165] 12975 13073 13171 13269 13367 13465 13563 13661 13759 13857 13955 14053
## [10177] 14151 14249 14347 14445 14543 14641 14739 14837 14935 15033 15131 15229
## [10189] 15327 15425 15523 15621 15719 15817 15915 16013 16111 16209 16307 16405
## [10201] 16503 16601 16699 16797 16895 16993 17091 17189 17287 17385 17483 17581
## [10213] 17679 17777 17875 17973 18071 18169 18267 18365 18463 18561 18659 18757
## [10225] 18855 18953 19051 19149 19247 19345 19443 19541 19639 19737 19835 19933
## [10237] 20031 20129 20227 20325 20423 20521 20619 20717 20815 20913 21011 21109
## [10249] 21207 21305 21403 21501 21599 21697 21795 21893 21991 22089 22187 22285
## [10261] 22383 22481 22579 22677 22775 22873 22971 23069 23167 23265 23363 23461
## [10273] 23559 23657 23755 23853 23951 24049 24147 24245 24343 24441 24539 24637
## [10285] 24735 24833 24931 25029 25127 25225 25323 25421 25519 25617 25715 25813
## [10297] 40 138 236 334 432 530 628 726 824 922 1020 1118
## [10309] 1216 1314 1412 1510 1608 1706 1804 1902 2000 2098 2196 2294
## [10321] 2392 2490 2588 2686 2784 2882 2980 3078 3176 3274 3372 3470
## [10333] 3568 3666 3764 3862 3960 4058 4156 4254 4352 4450 4548 4646
## [10345] 4744 4842 4940 5038 5136 5234 5332 5430 5528 5626 5724 5822
## [10357] 5920 6018 6116 6214 6312 6410 6508 6606 6704 6802 6900 6998
## [10369] 7096 7194 7292 7390 7488 7586 7684 7782 7880 7978 8076 8174
## [10381] 8272 8370 8468 8566 8664 8762 8860 8958 9056 9154 9252 9350
## [10393] 9448 9546 9644 9742 9840 9938 10036 10134 10232 10330 10428 10526
## [10405] 10624 10722 10820 10918 11016 11114 11212 11310 11408 11506 11604 11702
## [10417] 11800 11898 11996 12094 12192 12290 12388 12486 12584 12682 12780 12878
## [10429] 12976 13074 13172 13270 13368 13466 13564 13662 13760 13858 13956 14054
## [10441] 14152 14250 14348 14446 14544 14642 14740 14838 14936 15034 15132 15230
## [10453] 15328 15426 15524 15622 15720 15818 15916 16014 16112 16210 16308 16406
## [10465] 16504 16602 16700 16798 16896 16994 17092 17190 17288 17386 17484 17582
## [10477] 17680 17778 17876 17974 18072 18170 18268 18366 18464 18562 18660 18758
## [10489] 18856 18954 19052 19150 19248 19346 19444 19542 19640 19738 19836 19934
## [10501] 20032 20130 20228 20326 20424 20522 20620 20718 20816 20914 21012 21110
## [10513] 21208 21306 21404 21502 21600 21698 21796 21894 21992 22090 22188 22286
## [10525] 22384 22482 22580 22678 22776 22874 22972 23070 23168 23266 23364 23462
## [10537] 23560 23658 23756 23854 23952 24050 24148 24246 24344 24442 24540 24638
## [10549] 24736 24834 24932 25030 25128 25226 25324 25422 25520 25618 25716 25814
## [10561] 41 139 237 335 433 531 629 727 825 923 1021 1119
## [10573] 1217 1315 1413 1511 1609 1707 1805 1903 2001 2099 2197 2295
## [10585] 2393 2491 2589 2687 2785 2883 2981 3079 3177 3275 3373 3471
## [10597] 3569 3667 3765 3863 3961 4059 4157 4255 4353 4451 4549 4647
## [10609] 4745 4843 4941 5039 5137 5235 5333 5431 5529 5627 5725 5823
## [10621] 5921 6019 6117 6215 6313 6411 6509 6607 6705 6803 6901 6999
## [10633] 7097 7195 7293 7391 7489 7587 7685 7783 7881 7979 8077 8175
## [10645] 8273 8371 8469 8567 8665 8763 8861 8959 9057 9155 9253 9351
## [10657] 9449 9547 9645 9743 9841 9939 10037 10135 10233 10331 10429 10527
## [10669] 10625 10723 10821 10919 11017 11115 11213 11311 11409 11507 11605 11703
## [10681] 11801 11899 11997 12095 12193 12291 12389 12487 12585 12683 12781 12879
## [10693] 12977 13075 13173 13271 13369 13467 13565 13663 13761 13859 13957 14055
## [10705] 14153 14251 14349 14447 14545 14643 14741 14839 14937 15035 15133 15231
## [10717] 15329 15427 15525 15623 15721 15819 15917 16015 16113 16211 16309 16407
## [10729] 16505 16603 16701 16799 16897 16995 17093 17191 17289 17387 17485 17583
## [10741] 17681 17779 17877 17975 18073 18171 18269 18367 18465 18563 18661 18759
## [10753] 18857 18955 19053 19151 19249 19347 19445 19543 19641 19739 19837 19935
## [10765] 20033 20131 20229 20327 20425 20523 20621 20719 20817 20915 21013 21111
## [10777] 21209 21307 21405 21503 21601 21699 21797 21895 21993 22091 22189 22287
## [10789] 22385 22483 22581 22679 22777 22875 22973 23071 23169 23267 23365 23463
## [10801] 23561 23659 23757 23855 23953 24051 24149 24247 24345 24443 24541 24639
## [10813] 24737 24835 24933 25031 25129 25227 25325 25423 25521 25619 25717 25815
## [10825] 42 140 238 336 434 532 630 728 826 924 1022 1120
## [10837] 1218 1316 1414 1512 1610 1708 1806 1904 2002 2100 2198 2296
## [10849] 2394 2492 2590 2688 2786 2884 2982 3080 3178 3276 3374 3472
## [10861] 3570 3668 3766 3864 3962 4060 4158 4256 4354 4452 4550 4648
## [10873] 4746 4844 4942 5040 5138 5236 5334 5432 5530 5628 5726 5824
## [10885] 5922 6020 6118 6216 6314 6412 6510 6608 6706 6804 6902 7000
## [10897] 7098 7196 7294 7392 7490 7588 7686 7784 7882 7980 8078 8176
## [10909] 8274 8372 8470 8568 8666 8764 8862 8960 9058 9156 9254 9352
## [10921] 9450 9548 9646 9744 9842 9940 10038 10136 10234 10332 10430 10528
## [10933] 10626 10724 10822 10920 11018 11116 11214 11312 11410 11508 11606 11704
## [10945] 11802 11900 11998 12096 12194 12292 12390 12488 12586 12684 12782 12880
## [10957] 12978 13076 13174 13272 13370 13468 13566 13664 13762 13860 13958 14056
## [10969] 14154 14252 14350 14448 14546 14644 14742 14840 14938 15036 15134 15232
## [10981] 15330 15428 15526 15624 15722 15820 15918 16016 16114 16212 16310 16408
## [10993] 16506 16604 16702 16800 16898 16996 17094 17192 17290 17388 17486 17584
## [11005] 17682 17780 17878 17976 18074 18172 18270 18368 18466 18564 18662 18760
## [11017] 18858 18956 19054 19152 19250 19348 19446 19544 19642 19740 19838 19936
## [11029] 20034 20132 20230 20328 20426 20524 20622 20720 20818 20916 21014 21112
## [11041] 21210 21308 21406 21504 21602 21700 21798 21896 21994 22092 22190 22288
## [11053] 22386 22484 22582 22680 22778 22876 22974 23072 23170 23268 23366 23464
## [11065] 23562 23660 23758 23856 23954 24052 24150 24248 24346 24444 24542 24640
## [11077] 24738 24836 24934 25032 25130 25228 25326 25424 25522 25620 25718 25816
## [11089] 43 141 239 337 435 533 631 729 827 925 1023 1121
## [11101] 1219 1317 1415 1513 1611 1709 1807 1905 2003 2101 2199 2297
## [11113] 2395 2493 2591 2689 2787 2885 2983 3081 3179 3277 3375 3473
## [11125] 3571 3669 3767 3865 3963 4061 4159 4257 4355 4453 4551 4649
## [11137] 4747 4845 4943 5041 5139 5237 5335 5433 5531 5629 5727 5825
## [11149] 5923 6021 6119 6217 6315 6413 6511 6609 6707 6805 6903 7001
## [11161] 7099 7197 7295 7393 7491 7589 7687 7785 7883 7981 8079 8177
## [11173] 8275 8373 8471 8569 8667 8765 8863 8961 9059 9157 9255 9353
## [11185] 9451 9549 9647 9745 9843 9941 10039 10137 10235 10333 10431 10529
## [11197] 10627 10725 10823 10921 11019 11117 11215 11313 11411 11509 11607 11705
## [11209] 11803 11901 11999 12097 12195 12293 12391 12489 12587 12685 12783 12881
## [11221] 12979 13077 13175 13273 13371 13469 13567 13665 13763 13861 13959 14057
## [11233] 14155 14253 14351 14449 14547 14645 14743 14841 14939 15037 15135 15233
## [11245] 15331 15429 15527 15625 15723 15821 15919 16017 16115 16213 16311 16409
## [11257] 16507 16605 16703 16801 16899 16997 17095 17193 17291 17389 17487 17585
## [11269] 17683 17781 17879 17977 18075 18173 18271 18369 18467 18565 18663 18761
## [11281] 18859 18957 19055 19153 19251 19349 19447 19545 19643 19741 19839 19937
## [11293] 20035 20133 20231 20329 20427 20525 20623 20721 20819 20917 21015 21113
## [11305] 21211 21309 21407 21505 21603 21701 21799 21897 21995 22093 22191 22289
## [11317] 22387 22485 22583 22681 22779 22877 22975 23073 23171 23269 23367 23465
## [11329] 23563 23661 23759 23857 23955 24053 24151 24249 24347 24445 24543 24641
## [11341] 24739 24837 24935 25033 25131 25229 25327 25425 25523 25621 25719 25817
## [11353] 44 142 240 338 436 534 632 730 828 926 1024 1122
## [11365] 1220 1318 1416 1514 1612 1710 1808 1906 2004 2102 2200 2298
## [11377] 2396 2494 2592 2690 2788 2886 2984 3082 3180 3278 3376 3474
## [11389] 3572 3670 3768 3866 3964 4062 4160 4258 4356 4454 4552 4650
## [11401] 4748 4846 4944 5042 5140 5238 5336 5434 5532 5630 5728 5826
## [11413] 5924 6022 6120 6218 6316 6414 6512 6610 6708 6806 6904 7002
## [11425] 7100 7198 7296 7394 7492 7590 7688 7786 7884 7982 8080 8178
## [11437] 8276 8374 8472 8570 8668 8766 8864 8962 9060 9158 9256 9354
## [11449] 9452 9550 9648 9746 9844 9942 10040 10138 10236 10334 10432 10530
## [11461] 10628 10726 10824 10922 11020 11118 11216 11314 11412 11510 11608 11706
## [11473] 11804 11902 12000 12098 12196 12294 12392 12490 12588 12686 12784 12882
## [11485] 12980 13078 13176 13274 13372 13470 13568 13666 13764 13862 13960 14058
## [11497] 14156 14254 14352 14450 14548 14646 14744 14842 14940 15038 15136 15234
## [11509] 15332 15430 15528 15626 15724 15822 15920 16018 16116 16214 16312 16410
## [11521] 16508 16606 16704 16802 16900 16998 17096 17194 17292 17390 17488 17586
## [11533] 17684 17782 17880 17978 18076 18174 18272 18370 18468 18566 18664 18762
## [11545] 18860 18958 19056 19154 19252 19350 19448 19546 19644 19742 19840 19938
## [11557] 20036 20134 20232 20330 20428 20526 20624 20722 20820 20918 21016 21114
## [11569] 21212 21310 21408 21506 21604 21702 21800 21898 21996 22094 22192 22290
## [11581] 22388 22486 22584 22682 22780 22878 22976 23074 23172 23270 23368 23466
## [11593] 23564 23662 23760 23858 23956 24054 24152 24250 24348 24446 24544 24642
## [11605] 24740 24838 24936 25034 25132 25230 25328 25426 25524 25622 25720 25818
## [11617] 45 143 241 339 437 535 633 731 829 927 1025 1123
## [11629] 1221 1319 1417 1515 1613 1711 1809 1907 2005 2103 2201 2299
## [11641] 2397 2495 2593 2691 2789 2887 2985 3083 3181 3279 3377 3475
## [11653] 3573 3671 3769 3867 3965 4063 4161 4259 4357 4455 4553 4651
## [11665] 4749 4847 4945 5043 5141 5239 5337 5435 5533 5631 5729 5827
## [11677] 5925 6023 6121 6219 6317 6415 6513 6611 6709 6807 6905 7003
## [11689] 7101 7199 7297 7395 7493 7591 7689 7787 7885 7983 8081 8179
## [11701] 8277 8375 8473 8571 8669 8767 8865 8963 9061 9159 9257 9355
## [11713] 9453 9551 9649 9747 9845 9943 10041 10139 10237 10335 10433 10531
## [11725] 10629 10727 10825 10923 11021 11119 11217 11315 11413 11511 11609 11707
## [11737] 11805 11903 12001 12099 12197 12295 12393 12491 12589 12687 12785 12883
## [11749] 12981 13079 13177 13275 13373 13471 13569 13667 13765 13863 13961 14059
## [11761] 14157 14255 14353 14451 14549 14647 14745 14843 14941 15039 15137 15235
## [11773] 15333 15431 15529 15627 15725 15823 15921 16019 16117 16215 16313 16411
## [11785] 16509 16607 16705 16803 16901 16999 17097 17195 17293 17391 17489 17587
## [11797] 17685 17783 17881 17979 18077 18175 18273 18371 18469 18567 18665 18763
## [11809] 18861 18959 19057 19155 19253 19351 19449 19547 19645 19743 19841 19939
## [11821] 20037 20135 20233 20331 20429 20527 20625 20723 20821 20919 21017 21115
## [11833] 21213 21311 21409 21507 21605 21703 21801 21899 21997 22095 22193 22291
## [11845] 22389 22487 22585 22683 22781 22879 22977 23075 23173 23271 23369 23467
## [11857] 23565 23663 23761 23859 23957 24055 24153 24251 24349 24447 24545 24643
## [11869] 24741 24839 24937 25035 25133 25231 25329 25427 25525 25623 25721 25819
## [11881] 46 144 242 340 438 536 634 732 830 928 1026 1124
## [11893] 1222 1320 1418 1516 1614 1712 1810 1908 2006 2104 2202 2300
## [11905] 2398 2496 2594 2692 2790 2888 2986 3084 3182 3280 3378 3476
## [11917] 3574 3672 3770 3868 3966 4064 4162 4260 4358 4456 4554 4652
## [11929] 4750 4848 4946 5044 5142 5240 5338 5436 5534 5632 5730 5828
## [11941] 5926 6024 6122 6220 6318 6416 6514 6612 6710 6808 6906 7004
## [11953] 7102 7200 7298 7396 7494 7592 7690 7788 7886 7984 8082 8180
## [11965] 8278 8376 8474 8572 8670 8768 8866 8964 9062 9160 9258 9356
## [11977] 9454 9552 9650 9748 9846 9944 10042 10140 10238 10336 10434 10532
## [11989] 10630 10728 10826 10924 11022 11120 11218 11316 11414 11512 11610 11708
## [12001] 11806 11904 12002 12100 12198 12296 12394 12492 12590 12688 12786 12884
## [12013] 12982 13080 13178 13276 13374 13472 13570 13668 13766 13864 13962 14060
## [12025] 14158 14256 14354 14452 14550 14648 14746 14844 14942 15040 15138 15236
## [12037] 15334 15432 15530 15628 15726 15824 15922 16020 16118 16216 16314 16412
## [12049] 16510 16608 16706 16804 16902 17000 17098 17196 17294 17392 17490 17588
## [12061] 17686 17784 17882 17980 18078 18176 18274 18372 18470 18568 18666 18764
## [12073] 18862 18960 19058 19156 19254 19352 19450 19548 19646 19744 19842 19940
## [12085] 20038 20136 20234 20332 20430 20528 20626 20724 20822 20920 21018 21116
## [12097] 21214 21312 21410 21508 21606 21704 21802 21900 21998 22096 22194 22292
## [12109] 22390 22488 22586 22684 22782 22880 22978 23076 23174 23272 23370 23468
## [12121] 23566 23664 23762 23860 23958 24056 24154 24252 24350 24448 24546 24644
## [12133] 24742 24840 24938 25036 25134 25232 25330 25428 25526 25624 25722 25820
## [12145] 47 145 243 341 439 537 635 733 831 929 1027 1125
## [12157] 1223 1321 1419 1517 1615 1713 1811 1909 2007 2105 2203 2301
## [12169] 2399 2497 2595 2693 2791 2889 2987 3085 3183 3281 3379 3477
## [12181] 3575 3673 3771 3869 3967 4065 4163 4261 4359 4457 4555 4653
## [12193] 4751 4849 4947 5045 5143 5241 5339 5437 5535 5633 5731 5829
## [12205] 5927 6025 6123 6221 6319 6417 6515 6613 6711 6809 6907 7005
## [12217] 7103 7201 7299 7397 7495 7593 7691 7789 7887 7985 8083 8181
## [12229] 8279 8377 8475 8573 8671 8769 8867 8965 9063 9161 9259 9357
## [12241] 9455 9553 9651 9749 9847 9945 10043 10141 10239 10337 10435 10533
## [12253] 10631 10729 10827 10925 11023 11121 11219 11317 11415 11513 11611 11709
## [12265] 11807 11905 12003 12101 12199 12297 12395 12493 12591 12689 12787 12885
## [12277] 12983 13081 13179 13277 13375 13473 13571 13669 13767 13865 13963 14061
## [12289] 14159 14257 14355 14453 14551 14649 14747 14845 14943 15041 15139 15237
## [12301] 15335 15433 15531 15629 15727 15825 15923 16021 16119 16217 16315 16413
## [12313] 16511 16609 16707 16805 16903 17001 17099 17197 17295 17393 17491 17589
## [12325] 17687 17785 17883 17981 18079 18177 18275 18373 18471 18569 18667 18765
## [12337] 18863 18961 19059 19157 19255 19353 19451 19549 19647 19745 19843 19941
## [12349] 20039 20137 20235 20333 20431 20529 20627 20725 20823 20921 21019 21117
## [12361] 21215 21313 21411 21509 21607 21705 21803 21901 21999 22097 22195 22293
## [12373] 22391 22489 22587 22685 22783 22881 22979 23077 23175 23273 23371 23469
## [12385] 23567 23665 23763 23861 23959 24057 24155 24253 24351 24449 24547 24645
## [12397] 24743 24841 24939 25037 25135 25233 25331 25429 25527 25625 25723 25821
## [12409] 48 146 244 342 440 538 636 734 832 930 1028 1126
## [12421] 1224 1322 1420 1518 1616 1714 1812 1910 2008 2106 2204 2302
## [12433] 2400 2498 2596 2694 2792 2890 2988 3086 3184 3282 3380 3478
## [12445] 3576 3674 3772 3870 3968 4066 4164 4262 4360 4458 4556 4654
## [12457] 4752 4850 4948 5046 5144 5242 5340 5438 5536 5634 5732 5830
## [12469] 5928 6026 6124 6222 6320 6418 6516 6614 6712 6810 6908 7006
## [12481] 7104 7202 7300 7398 7496 7594 7692 7790 7888 7986 8084 8182
## [12493] 8280 8378 8476 8574 8672 8770 8868 8966 9064 9162 9260 9358
## [12505] 9456 9554 9652 9750 9848 9946 10044 10142 10240 10338 10436 10534
## [12517] 10632 10730 10828 10926 11024 11122 11220 11318 11416 11514 11612 11710
## [12529] 11808 11906 12004 12102 12200 12298 12396 12494 12592 12690 12788 12886
## [12541] 12984 13082 13180 13278 13376 13474 13572 13670 13768 13866 13964 14062
## [12553] 14160 14258 14356 14454 14552 14650 14748 14846 14944 15042 15140 15238
## [12565] 15336 15434 15532 15630 15728 15826 15924 16022 16120 16218 16316 16414
## [12577] 16512 16610 16708 16806 16904 17002 17100 17198 17296 17394 17492 17590
## [12589] 17688 17786 17884 17982 18080 18178 18276 18374 18472 18570 18668 18766
## [12601] 18864 18962 19060 19158 19256 19354 19452 19550 19648 19746 19844 19942
## [12613] 20040 20138 20236 20334 20432 20530 20628 20726 20824 20922 21020 21118
## [12625] 21216 21314 21412 21510 21608 21706 21804 21902 22000 22098 22196 22294
## [12637] 22392 22490 22588 22686 22784 22882 22980 23078 23176 23274 23372 23470
## [12649] 23568 23666 23764 23862 23960 24058 24156 24254 24352 24450 24548 24646
## [12661] 24744 24842 24940 25038 25136 25234 25332 25430 25528 25626 25724 25822
## [12673] 49 147 245 343 441 539 637 735 833 931 1029 1127
## [12685] 1225 1323 1421 1519 1617 1715 1813 1911 2009 2107 2205 2303
## [12697] 2401 2499 2597 2695 2793 2891 2989 3087 3185 3283 3381 3479
## [12709] 3577 3675 3773 3871 3969 4067 4165 4263 4361 4459 4557 4655
## [12721] 4753 4851 4949 5047 5145 5243 5341 5439 5537 5635 5733 5831
## [12733] 5929 6027 6125 6223 6321 6419 6517 6615 6713 6811 6909 7007
## [12745] 7105 7203 7301 7399 7497 7595 7693 7791 7889 7987 8085 8183
## [12757] 8281 8379 8477 8575 8673 8771 8869 8967 9065 9163 9261 9359
## [12769] 9457 9555 9653 9751 9849 9947 10045 10143 10241 10339 10437 10535
## [12781] 10633 10731 10829 10927 11025 11123 11221 11319 11417 11515 11613 11711
## [12793] 11809 11907 12005 12103 12201 12299 12397 12495 12593 12691 12789 12887
## [12805] 12985 13083 13181 13279 13377 13475 13573 13671 13769 13867 13965 14063
## [12817] 14161 14259 14357 14455 14553 14651 14749 14847 14945 15043 15141 15239
## [12829] 15337 15435 15533 15631 15729 15827 15925 16023 16121 16219 16317 16415
## [12841] 16513 16611 16709 16807 16905 17003 17101 17199 17297 17395 17493 17591
## [12853] 17689 17787 17885 17983 18081 18179 18277 18375 18473 18571 18669 18767
## [12865] 18865 18963 19061 19159 19257 19355 19453 19551 19649 19747 19845 19943
## [12877] 20041 20139 20237 20335 20433 20531 20629 20727 20825 20923 21021 21119
## [12889] 21217 21315 21413 21511 21609 21707 21805 21903 22001 22099 22197 22295
## [12901] 22393 22491 22589 22687 22785 22883 22981 23079 23177 23275 23373 23471
## [12913] 23569 23667 23765 23863 23961 24059 24157 24255 24353 24451 24549 24647
## [12925] 24745 24843 24941 25039 25137 25235 25333 25431 25529 25627 25725 25823
## [12937] 50 148 246 344 442 540 638 736 834 932 1030 1128
## [12949] 1226 1324 1422 1520 1618 1716 1814 1912 2010 2108 2206 2304
## [12961] 2402 2500 2598 2696 2794 2892 2990 3088 3186 3284 3382 3480
## [12973] 3578 3676 3774 3872 3970 4068 4166 4264 4362 4460 4558 4656
## [12985] 4754 4852 4950 5048 5146 5244 5342 5440 5538 5636 5734 5832
## [12997] 5930 6028 6126 6224 6322 6420 6518 6616 6714 6812 6910 7008
## [13009] 7106 7204 7302 7400 7498 7596 7694 7792 7890 7988 8086 8184
## [13021] 8282 8380 8478 8576 8674 8772 8870 8968 9066 9164 9262 9360
## [13033] 9458 9556 9654 9752 9850 9948 10046 10144 10242 10340 10438 10536
## [13045] 10634 10732 10830 10928 11026 11124 11222 11320 11418 11516 11614 11712
## [13057] 11810 11908 12006 12104 12202 12300 12398 12496 12594 12692 12790 12888
## [13069] 12986 13084 13182 13280 13378 13476 13574 13672 13770 13868 13966 14064
## [13081] 14162 14260 14358 14456 14554 14652 14750 14848 14946 15044 15142 15240
## [13093] 15338 15436 15534 15632 15730 15828 15926 16024 16122 16220 16318 16416
## [13105] 16514 16612 16710 16808 16906 17004 17102 17200 17298 17396 17494 17592
## [13117] 17690 17788 17886 17984 18082 18180 18278 18376 18474 18572 18670 18768
## [13129] 18866 18964 19062 19160 19258 19356 19454 19552 19650 19748 19846 19944
## [13141] 20042 20140 20238 20336 20434 20532 20630 20728 20826 20924 21022 21120
## [13153] 21218 21316 21414 21512 21610 21708 21806 21904 22002 22100 22198 22296
## [13165] 22394 22492 22590 22688 22786 22884 22982 23080 23178 23276 23374 23472
## [13177] 23570 23668 23766 23864 23962 24060 24158 24256 24354 24452 24550 24648
## [13189] 24746 24844 24942 25040 25138 25236 25334 25432 25530 25628 25726 25824
## [13201] 51 149 247 345 443 541 639 737 835 933 1031 1129
## [13213] 1227 1325 1423 1521 1619 1717 1815 1913 2011 2109 2207 2305
## [13225] 2403 2501 2599 2697 2795 2893 2991 3089 3187 3285 3383 3481
## [13237] 3579 3677 3775 3873 3971 4069 4167 4265 4363 4461 4559 4657
## [13249] 4755 4853 4951 5049 5147 5245 5343 5441 5539 5637 5735 5833
## [13261] 5931 6029 6127 6225 6323 6421 6519 6617 6715 6813 6911 7009
## [13273] 7107 7205 7303 7401 7499 7597 7695 7793 7891 7989 8087 8185
## [13285] 8283 8381 8479 8577 8675 8773 8871 8969 9067 9165 9263 9361
## [13297] 9459 9557 9655 9753 9851 9949 10047 10145 10243 10341 10439 10537
## [13309] 10635 10733 10831 10929 11027 11125 11223 11321 11419 11517 11615 11713
## [13321] 11811 11909 12007 12105 12203 12301 12399 12497 12595 12693 12791 12889
## [13333] 12987 13085 13183 13281 13379 13477 13575 13673 13771 13869 13967 14065
## [13345] 14163 14261 14359 14457 14555 14653 14751 14849 14947 15045 15143 15241
## [13357] 15339 15437 15535 15633 15731 15829 15927 16025 16123 16221 16319 16417
## [13369] 16515 16613 16711 16809 16907 17005 17103 17201 17299 17397 17495 17593
## [13381] 17691 17789 17887 17985 18083 18181 18279 18377 18475 18573 18671 18769
## [13393] 18867 18965 19063 19161 19259 19357 19455 19553 19651 19749 19847 19945
## [13405] 20043 20141 20239 20337 20435 20533 20631 20729 20827 20925 21023 21121
## [13417] 21219 21317 21415 21513 21611 21709 21807 21905 22003 22101 22199 22297
## [13429] 22395 22493 22591 22689 22787 22885 22983 23081 23179 23277 23375 23473
## [13441] 23571 23669 23767 23865 23963 24061 24159 24257 24355 24453 24551 24649
## [13453] 24747 24845 24943 25041 25139 25237 25335 25433 25531 25629 25727 25825
## [13465] 52 150 248 346 444 542 640 738 836 934 1032 1130
## [13477] 1228 1326 1424 1522 1620 1718 1816 1914 2012 2110 2208 2306
## [13489] 2404 2502 2600 2698 2796 2894 2992 3090 3188 3286 3384 3482
## [13501] 3580 3678 3776 3874 3972 4070 4168 4266 4364 4462 4560 4658
## [13513] 4756 4854 4952 5050 5148 5246 5344 5442 5540 5638 5736 5834
## [13525] 5932 6030 6128 6226 6324 6422 6520 6618 6716 6814 6912 7010
## [13537] 7108 7206 7304 7402 7500 7598 7696 7794 7892 7990 8088 8186
## [13549] 8284 8382 8480 8578 8676 8774 8872 8970 9068 9166 9264 9362
## [13561] 9460 9558 9656 9754 9852 9950 10048 10146 10244 10342 10440 10538
## [13573] 10636 10734 10832 10930 11028 11126 11224 11322 11420 11518 11616 11714
## [13585] 11812 11910 12008 12106 12204 12302 12400 12498 12596 12694 12792 12890
## [13597] 12988 13086 13184 13282 13380 13478 13576 13674 13772 13870 13968 14066
## [13609] 14164 14262 14360 14458 14556 14654 14752 14850 14948 15046 15144 15242
## [13621] 15340 15438 15536 15634 15732 15830 15928 16026 16124 16222 16320 16418
## [13633] 16516 16614 16712 16810 16908 17006 17104 17202 17300 17398 17496 17594
## [13645] 17692 17790 17888 17986 18084 18182 18280 18378 18476 18574 18672 18770
## [13657] 18868 18966 19064 19162 19260 19358 19456 19554 19652 19750 19848 19946
## [13669] 20044 20142 20240 20338 20436 20534 20632 20730 20828 20926 21024 21122
## [13681] 21220 21318 21416 21514 21612 21710 21808 21906 22004 22102 22200 22298
## [13693] 22396 22494 22592 22690 22788 22886 22984 23082 23180 23278 23376 23474
## [13705] 23572 23670 23768 23866 23964 24062 24160 24258 24356 24454 24552 24650
## [13717] 24748 24846 24944 25042 25140 25238 25336 25434 25532 25630 25728 25826
## [13729] 53 151 249 347 445 543 641 739 837 935 1033 1131
## [13741] 1229 1327 1425 1523 1621 1719 1817 1915 2013 2111 2209 2307
## [13753] 2405 2503 2601 2699 2797 2895 2993 3091 3189 3287 3385 3483
## [13765] 3581 3679 3777 3875 3973 4071 4169 4267 4365 4463 4561 4659
## [13777] 4757 4855 4953 5051 5149 5247 5345 5443 5541 5639 5737 5835
## [13789] 5933 6031 6129 6227 6325 6423 6521 6619 6717 6815 6913 7011
## [13801] 7109 7207 7305 7403 7501 7599 7697 7795 7893 7991 8089 8187
## [13813] 8285 8383 8481 8579 8677 8775 8873 8971 9069 9167 9265 9363
## [13825] 9461 9559 9657 9755 9853 9951 10049 10147 10245 10343 10441 10539
## [13837] 10637 10735 10833 10931 11029 11127 11225 11323 11421 11519 11617 11715
## [13849] 11813 11911 12009 12107 12205 12303 12401 12499 12597 12695 12793 12891
## [13861] 12989 13087 13185 13283 13381 13479 13577 13675 13773 13871 13969 14067
## [13873] 14165 14263 14361 14459 14557 14655 14753 14851 14949 15047 15145 15243
## [13885] 15341 15439 15537 15635 15733 15831 15929 16027 16125 16223 16321 16419
## [13897] 16517 16615 16713 16811 16909 17007 17105 17203 17301 17399 17497 17595
## [13909] 17693 17791 17889 17987 18085 18183 18281 18379 18477 18575 18673 18771
## [13921] 18869 18967 19065 19163 19261 19359 19457 19555 19653 19751 19849 19947
## [13933] 20045 20143 20241 20339 20437 20535 20633 20731 20829 20927 21025 21123
## [13945] 21221 21319 21417 21515 21613 21711 21809 21907 22005 22103 22201 22299
## [13957] 22397 22495 22593 22691 22789 22887 22985 23083 23181 23279 23377 23475
## [13969] 23573 23671 23769 23867 23965 24063 24161 24259 24357 24455 24553 24651
## [13981] 24749 24847 24945 25043 25141 25239 25337 25435 25533 25631 25729 25827
## [13993] 54 152 250 348 446 544 642 740 838 936 1034 1132
## [14005] 1230 1328 1426 1524 1622 1720 1818 1916 2014 2112 2210 2308
## [14017] 2406 2504 2602 2700 2798 2896 2994 3092 3190 3288 3386 3484
## [14029] 3582 3680 3778 3876 3974 4072 4170 4268 4366 4464 4562 4660
## [14041] 4758 4856 4954 5052 5150 5248 5346 5444 5542 5640 5738 5836
## [14053] 5934 6032 6130 6228 6326 6424 6522 6620 6718 6816 6914 7012
## [14065] 7110 7208 7306 7404 7502 7600 7698 7796 7894 7992 8090 8188
## [14077] 8286 8384 8482 8580 8678 8776 8874 8972 9070 9168 9266 9364
## [14089] 9462 9560 9658 9756 9854 9952 10050 10148 10246 10344 10442 10540
## [14101] 10638 10736 10834 10932 11030 11128 11226 11324 11422 11520 11618 11716
## [14113] 11814 11912 12010 12108 12206 12304 12402 12500 12598 12696 12794 12892
## [14125] 12990 13088 13186 13284 13382 13480 13578 13676 13774 13872 13970 14068
## [14137] 14166 14264 14362 14460 14558 14656 14754 14852 14950 15048 15146 15244
## [14149] 15342 15440 15538 15636 15734 15832 15930 16028 16126 16224 16322 16420
## [14161] 16518 16616 16714 16812 16910 17008 17106 17204 17302 17400 17498 17596
## [14173] 17694 17792 17890 17988 18086 18184 18282 18380 18478 18576 18674 18772
## [14185] 18870 18968 19066 19164 19262 19360 19458 19556 19654 19752 19850 19948
## [14197] 20046 20144 20242 20340 20438 20536 20634 20732 20830 20928 21026 21124
## [14209] 21222 21320 21418 21516 21614 21712 21810 21908 22006 22104 22202 22300
## [14221] 22398 22496 22594 22692 22790 22888 22986 23084 23182 23280 23378 23476
## [14233] 23574 23672 23770 23868 23966 24064 24162 24260 24358 24456 24554 24652
## [14245] 24750 24848 24946 25044 25142 25240 25338 25436 25534 25632 25730 25828
## [14257] 55 153 251 349 447 545 643 741 839 937 1035 1133
## [14269] 1231 1329 1427 1525 1623 1721 1819 1917 2015 2113 2211 2309
## [14281] 2407 2505 2603 2701 2799 2897 2995 3093 3191 3289 3387 3485
## [14293] 3583 3681 3779 3877 3975 4073 4171 4269 4367 4465 4563 4661
## [14305] 4759 4857 4955 5053 5151 5249 5347 5445 5543 5641 5739 5837
## [14317] 5935 6033 6131 6229 6327 6425 6523 6621 6719 6817 6915 7013
## [14329] 7111 7209 7307 7405 7503 7601 7699 7797 7895 7993 8091 8189
## [14341] 8287 8385 8483 8581 8679 8777 8875 8973 9071 9169 9267 9365
## [14353] 9463 9561 9659 9757 9855 9953 10051 10149 10247 10345 10443 10541
## [14365] 10639 10737 10835 10933 11031 11129 11227 11325 11423 11521 11619 11717
## [14377] 11815 11913 12011 12109 12207 12305 12403 12501 12599 12697 12795 12893
## [14389] 12991 13089 13187 13285 13383 13481 13579 13677 13775 13873 13971 14069
## [14401] 14167 14265 14363 14461 14559 14657 14755 14853 14951 15049 15147 15245
## [14413] 15343 15441 15539 15637 15735 15833 15931 16029 16127 16225 16323 16421
## [14425] 16519 16617 16715 16813 16911 17009 17107 17205 17303 17401 17499 17597
## [14437] 17695 17793 17891 17989 18087 18185 18283 18381 18479 18577 18675 18773
## [14449] 18871 18969 19067 19165 19263 19361 19459 19557 19655 19753 19851 19949
## [14461] 20047 20145 20243 20341 20439 20537 20635 20733 20831 20929 21027 21125
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## [14521] 56 154 252 350 448 546 644 742 840 938 1036 1134
## [14533] 1232 1330 1428 1526 1624 1722 1820 1918 2016 2114 2212 2310
## [14545] 2408 2506 2604 2702 2800 2898 2996 3094 3192 3290 3388 3486
## [14557] 3584 3682 3780 3878 3976 4074 4172 4270 4368 4466 4564 4662
## [14569] 4760 4858 4956 5054 5152 5250 5348 5446 5544 5642 5740 5838
## [14581] 5936 6034 6132 6230 6328 6426 6524 6622 6720 6818 6916 7014
## [14593] 7112 7210 7308 7406 7504 7602 7700 7798 7896 7994 8092 8190
## [14605] 8288 8386 8484 8582 8680 8778 8876 8974 9072 9170 9268 9366
## [14617] 9464 9562 9660 9758 9856 9954 10052 10150 10248 10346 10444 10542
## [14629] 10640 10738 10836 10934 11032 11130 11228 11326 11424 11522 11620 11718
## [14641] 11816 11914 12012 12110 12208 12306 12404 12502 12600 12698 12796 12894
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## [14689] 16520 16618 16716 16814 16912 17010 17108 17206 17304 17402 17500 17598
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## [14785] 57 155 253 351 449 547 645 743 841 939 1037 1135
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## [14881] 9465 9563 9661 9759 9857 9955 10053 10151 10249 10347 10445 10543
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## [14905] 11817 11915 12013 12111 12209 12307 12405 12503 12601 12699 12797 12895
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## [15061] 1234 1332 1430 1528 1626 1724 1822 1920 2018 2116 2214 2312
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## [15313] 59 157 255 353 451 549 647 745 843 941 1039 1137
## [15325] 1235 1333 1431 1529 1627 1725 1823 1921 2019 2117 2215 2313
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## [17629] 20059 20157 20255 20353 20451 20549 20647 20745 20843 20941 21039 21137
## [17641] 21235 21333 21431 21529 21627 21725 21823 21921 22019 22117 22215 22313
## [17653] 22411 22509 22607 22705 22803 22901 22999 23097 23195 23293 23391 23489
## [17665] 23587 23685 23783 23881 23979 24077 24175 24273 24371 24469 24567 24665
## [17677] 24763 24861 24959 25057 25155 25253 25351 25449 25547 25645 25743 25841
## [17689] 68 166 264 362 460 558 656 754 852 950 1048 1146
## [17701] 1244 1342 1440 1538 1636 1734 1832 1930 2028 2126 2224 2322
## [17713] 2420 2518 2616 2714 2812 2910 3008 3106 3204 3302 3400 3498
## [17725] 3596 3694 3792 3890 3988 4086 4184 4282 4380 4478 4576 4674
## [17737] 4772 4870 4968 5066 5164 5262 5360 5458 5556 5654 5752 5850
## [17749] 5948 6046 6144 6242 6340 6438 6536 6634 6732 6830 6928 7026
## [17761] 7124 7222 7320 7418 7516 7614 7712 7810 7908 8006 8104 8202
## [17773] 8300 8398 8496 8594 8692 8790 8888 8986 9084 9182 9280 9378
## [17785] 9476 9574 9672 9770 9868 9966 10064 10162 10260 10358 10456 10554
## [17797] 10652 10750 10848 10946 11044 11142 11240 11338 11436 11534 11632 11730
## [17809] 11828 11926 12024 12122 12220 12318 12416 12514 12612 12710 12808 12906
## [17821] 13004 13102 13200 13298 13396 13494 13592 13690 13788 13886 13984 14082
## [17833] 14180 14278 14376 14474 14572 14670 14768 14866 14964 15062 15160 15258
## [17845] 15356 15454 15552 15650 15748 15846 15944 16042 16140 16238 16336 16434
## [17857] 16532 16630 16728 16826 16924 17022 17120 17218 17316 17414 17512 17610
## [17869] 17708 17806 17904 18002 18100 18198 18296 18394 18492 18590 18688 18786
## [17881] 18884 18982 19080 19178 19276 19374 19472 19570 19668 19766 19864 19962
## [17893] 20060 20158 20256 20354 20452 20550 20648 20746 20844 20942 21040 21138
## [17905] 21236 21334 21432 21530 21628 21726 21824 21922 22020 22118 22216 22314
## [17917] 22412 22510 22608 22706 22804 22902 23000 23098 23196 23294 23392 23490
## [17929] 23588 23686 23784 23882 23980 24078 24176 24274 24372 24470 24568 24666
## [17941] 24764 24862 24960 25058 25156 25254 25352 25450 25548 25646 25744 25842
## [17953] 69 167 265 363 461 559 657 755 853 951 1049 1147
## [17965] 1245 1343 1441 1539 1637 1735 1833 1931 2029 2127 2225 2323
## [17977] 2421 2519 2617 2715 2813 2911 3009 3107 3205 3303 3401 3499
## [17989] 3597 3695 3793 3891 3989 4087 4185 4283 4381 4479 4577 4675
## [18001] 4773 4871 4969 5067 5165 5263 5361 5459 5557 5655 5753 5851
## [18013] 5949 6047 6145 6243 6341 6439 6537 6635 6733 6831 6929 7027
## [18025] 7125 7223 7321 7419 7517 7615 7713 7811 7909 8007 8105 8203
## [18037] 8301 8399 8497 8595 8693 8791 8889 8987 9085 9183 9281 9379
## [18049] 9477 9575 9673 9771 9869 9967 10065 10163 10261 10359 10457 10555
## [18061] 10653 10751 10849 10947 11045 11143 11241 11339 11437 11535 11633 11731
## [18073] 11829 11927 12025 12123 12221 12319 12417 12515 12613 12711 12809 12907
## [18085] 13005 13103 13201 13299 13397 13495 13593 13691 13789 13887 13985 14083
## [18097] 14181 14279 14377 14475 14573 14671 14769 14867 14965 15063 15161 15259
## [18109] 15357 15455 15553 15651 15749 15847 15945 16043 16141 16239 16337 16435
## [18121] 16533 16631 16729 16827 16925 17023 17121 17219 17317 17415 17513 17611
## [18133] 17709 17807 17905 18003 18101 18199 18297 18395 18493 18591 18689 18787
## [18145] 18885 18983 19081 19179 19277 19375 19473 19571 19669 19767 19865 19963
## [18157] 20061 20159 20257 20355 20453 20551 20649 20747 20845 20943 21041 21139
## [18169] 21237 21335 21433 21531 21629 21727 21825 21923 22021 22119 22217 22315
## [18181] 22413 22511 22609 22707 22805 22903 23001 23099 23197 23295 23393 23491
## [18193] 23589 23687 23785 23883 23981 24079 24177 24275 24373 24471 24569 24667
## [18205] 24765 24863 24961 25059 25157 25255 25353 25451 25549 25647 25745 25843
## [18217] 70 168 266 364 462 560 658 756 854 952 1050 1148
## [18229] 1246 1344 1442 1540 1638 1736 1834 1932 2030 2128 2226 2324
## [18241] 2422 2520 2618 2716 2814 2912 3010 3108 3206 3304 3402 3500
## [18253] 3598 3696 3794 3892 3990 4088 4186 4284 4382 4480 4578 4676
## [18265] 4774 4872 4970 5068 5166 5264 5362 5460 5558 5656 5754 5852
## [18277] 5950 6048 6146 6244 6342 6440 6538 6636 6734 6832 6930 7028
## [18289] 7126 7224 7322 7420 7518 7616 7714 7812 7910 8008 8106 8204
## [18301] 8302 8400 8498 8596 8694 8792 8890 8988 9086 9184 9282 9380
## [18313] 9478 9576 9674 9772 9870 9968 10066 10164 10262 10360 10458 10556
## [18325] 10654 10752 10850 10948 11046 11144 11242 11340 11438 11536 11634 11732
## [18337] 11830 11928 12026 12124 12222 12320 12418 12516 12614 12712 12810 12908
## [18349] 13006 13104 13202 13300 13398 13496 13594 13692 13790 13888 13986 14084
## [18361] 14182 14280 14378 14476 14574 14672 14770 14868 14966 15064 15162 15260
## [18373] 15358 15456 15554 15652 15750 15848 15946 16044 16142 16240 16338 16436
## [18385] 16534 16632 16730 16828 16926 17024 17122 17220 17318 17416 17514 17612
## [18397] 17710 17808 17906 18004 18102 18200 18298 18396 18494 18592 18690 18788
## [18409] 18886 18984 19082 19180 19278 19376 19474 19572 19670 19768 19866 19964
## [18421] 20062 20160 20258 20356 20454 20552 20650 20748 20846 20944 21042 21140
## [18433] 21238 21336 21434 21532 21630 21728 21826 21924 22022 22120 22218 22316
## [18445] 22414 22512 22610 22708 22806 22904 23002 23100 23198 23296 23394 23492
## [18457] 23590 23688 23786 23884 23982 24080 24178 24276 24374 24472 24570 24668
## [18469] 24766 24864 24962 25060 25158 25256 25354 25452 25550 25648 25746 25844
## [18481] 71 169 267 365 463 561 659 757 855 953 1051 1149
## [18493] 1247 1345 1443 1541 1639 1737 1835 1933 2031 2129 2227 2325
## [18505] 2423 2521 2619 2717 2815 2913 3011 3109 3207 3305 3403 3501
## [18517] 3599 3697 3795 3893 3991 4089 4187 4285 4383 4481 4579 4677
## [18529] 4775 4873 4971 5069 5167 5265 5363 5461 5559 5657 5755 5853
## [18541] 5951 6049 6147 6245 6343 6441 6539 6637 6735 6833 6931 7029
## [18553] 7127 7225 7323 7421 7519 7617 7715 7813 7911 8009 8107 8205
## [18565] 8303 8401 8499 8597 8695 8793 8891 8989 9087 9185 9283 9381
## [18577] 9479 9577 9675 9773 9871 9969 10067 10165 10263 10361 10459 10557
## [18589] 10655 10753 10851 10949 11047 11145 11243 11341 11439 11537 11635 11733
## [18601] 11831 11929 12027 12125 12223 12321 12419 12517 12615 12713 12811 12909
## [18613] 13007 13105 13203 13301 13399 13497 13595 13693 13791 13889 13987 14085
## [18625] 14183 14281 14379 14477 14575 14673 14771 14869 14967 15065 15163 15261
## [18637] 15359 15457 15555 15653 15751 15849 15947 16045 16143 16241 16339 16437
## [18649] 16535 16633 16731 16829 16927 17025 17123 17221 17319 17417 17515 17613
## [18661] 17711 17809 17907 18005 18103 18201 18299 18397 18495 18593 18691 18789
## [18673] 18887 18985 19083 19181 19279 19377 19475 19573 19671 19769 19867 19965
## [18685] 20063 20161 20259 20357 20455 20553 20651 20749 20847 20945 21043 21141
## [18697] 21239 21337 21435 21533 21631 21729 21827 21925 22023 22121 22219 22317
## [18709] 22415 22513 22611 22709 22807 22905 23003 23101 23199 23297 23395 23493
## [18721] 23591 23689 23787 23885 23983 24081 24179 24277 24375 24473 24571 24669
## [18733] 24767 24865 24963 25061 25159 25257 25355 25453 25551 25649 25747 25845
## [18745] 72 170 268 366 464 562 660 758 856 954 1052 1150
## [18757] 1248 1346 1444 1542 1640 1738 1836 1934 2032 2130 2228 2326
## [18769] 2424 2522 2620 2718 2816 2914 3012 3110 3208 3306 3404 3502
## [18781] 3600 3698 3796 3894 3992 4090 4188 4286 4384 4482 4580 4678
## [18793] 4776 4874 4972 5070 5168 5266 5364 5462 5560 5658 5756 5854
## [18805] 5952 6050 6148 6246 6344 6442 6540 6638 6736 6834 6932 7030
## [18817] 7128 7226 7324 7422 7520 7618 7716 7814 7912 8010 8108 8206
## [18829] 8304 8402 8500 8598 8696 8794 8892 8990 9088 9186 9284 9382
## [18841] 9480 9578 9676 9774 9872 9970 10068 10166 10264 10362 10460 10558
## [18853] 10656 10754 10852 10950 11048 11146 11244 11342 11440 11538 11636 11734
## [18865] 11832 11930 12028 12126 12224 12322 12420 12518 12616 12714 12812 12910
## [18877] 13008 13106 13204 13302 13400 13498 13596 13694 13792 13890 13988 14086
## [18889] 14184 14282 14380 14478 14576 14674 14772 14870 14968 15066 15164 15262
## [18901] 15360 15458 15556 15654 15752 15850 15948 16046 16144 16242 16340 16438
## [18913] 16536 16634 16732 16830 16928 17026 17124 17222 17320 17418 17516 17614
## [18925] 17712 17810 17908 18006 18104 18202 18300 18398 18496 18594 18692 18790
## [18937] 18888 18986 19084 19182 19280 19378 19476 19574 19672 19770 19868 19966
## [18949] 20064 20162 20260 20358 20456 20554 20652 20750 20848 20946 21044 21142
## [18961] 21240 21338 21436 21534 21632 21730 21828 21926 22024 22122 22220 22318
## [18973] 22416 22514 22612 22710 22808 22906 23004 23102 23200 23298 23396 23494
## [18985] 23592 23690 23788 23886 23984 24082 24180 24278 24376 24474 24572 24670
## [18997] 24768 24866 24964 25062 25160 25258 25356 25454 25552 25650 25748 25846
## [19009] 73 171 269 367 465 563 661 759 857 955 1053 1151
## [19021] 1249 1347 1445 1543 1641 1739 1837 1935 2033 2131 2229 2327
## [19033] 2425 2523 2621 2719 2817 2915 3013 3111 3209 3307 3405 3503
## [19045] 3601 3699 3797 3895 3993 4091 4189 4287 4385 4483 4581 4679
## [19057] 4777 4875 4973 5071 5169 5267 5365 5463 5561 5659 5757 5855
## [19069] 5953 6051 6149 6247 6345 6443 6541 6639 6737 6835 6933 7031
## [19081] 7129 7227 7325 7423 7521 7619 7717 7815 7913 8011 8109 8207
## [19093] 8305 8403 8501 8599 8697 8795 8893 8991 9089 9187 9285 9383
## [19105] 9481 9579 9677 9775 9873 9971 10069 10167 10265 10363 10461 10559
## [19117] 10657 10755 10853 10951 11049 11147 11245 11343 11441 11539 11637 11735
## [19129] 11833 11931 12029 12127 12225 12323 12421 12519 12617 12715 12813 12911
## [19141] 13009 13107 13205 13303 13401 13499 13597 13695 13793 13891 13989 14087
## [19153] 14185 14283 14381 14479 14577 14675 14773 14871 14969 15067 15165 15263
## [19165] 15361 15459 15557 15655 15753 15851 15949 16047 16145 16243 16341 16439
## [19177] 16537 16635 16733 16831 16929 17027 17125 17223 17321 17419 17517 17615
## [19189] 17713 17811 17909 18007 18105 18203 18301 18399 18497 18595 18693 18791
## [19201] 18889 18987 19085 19183 19281 19379 19477 19575 19673 19771 19869 19967
## [19213] 20065 20163 20261 20359 20457 20555 20653 20751 20849 20947 21045 21143
## [19225] 21241 21339 21437 21535 21633 21731 21829 21927 22025 22123 22221 22319
## [19237] 22417 22515 22613 22711 22809 22907 23005 23103 23201 23299 23397 23495
## [19249] 23593 23691 23789 23887 23985 24083 24181 24279 24377 24475 24573 24671
## [19261] 24769 24867 24965 25063 25161 25259 25357 25455 25553 25651 25749 25847
## [19273] 74 172 270 368 466 564 662 760 858 956 1054 1152
## [19285] 1250 1348 1446 1544 1642 1740 1838 1936 2034 2132 2230 2328
## [19297] 2426 2524 2622 2720 2818 2916 3014 3112 3210 3308 3406 3504
## [19309] 3602 3700 3798 3896 3994 4092 4190 4288 4386 4484 4582 4680
## [19321] 4778 4876 4974 5072 5170 5268 5366 5464 5562 5660 5758 5856
## [19333] 5954 6052 6150 6248 6346 6444 6542 6640 6738 6836 6934 7032
## [19345] 7130 7228 7326 7424 7522 7620 7718 7816 7914 8012 8110 8208
## [19357] 8306 8404 8502 8600 8698 8796 8894 8992 9090 9188 9286 9384
## [19369] 9482 9580 9678 9776 9874 9972 10070 10168 10266 10364 10462 10560
## [19381] 10658 10756 10854 10952 11050 11148 11246 11344 11442 11540 11638 11736
## [19393] 11834 11932 12030 12128 12226 12324 12422 12520 12618 12716 12814 12912
## [19405] 13010 13108 13206 13304 13402 13500 13598 13696 13794 13892 13990 14088
## [19417] 14186 14284 14382 14480 14578 14676 14774 14872 14970 15068 15166 15264
## [19429] 15362 15460 15558 15656 15754 15852 15950 16048 16146 16244 16342 16440
## [19441] 16538 16636 16734 16832 16930 17028 17126 17224 17322 17420 17518 17616
## [19453] 17714 17812 17910 18008 18106 18204 18302 18400 18498 18596 18694 18792
## [19465] 18890 18988 19086 19184 19282 19380 19478 19576 19674 19772 19870 19968
## [19477] 20066 20164 20262 20360 20458 20556 20654 20752 20850 20948 21046 21144
## [19489] 21242 21340 21438 21536 21634 21732 21830 21928 22026 22124 22222 22320
## [19501] 22418 22516 22614 22712 22810 22908 23006 23104 23202 23300 23398 23496
## [19513] 23594 23692 23790 23888 23986 24084 24182 24280 24378 24476 24574 24672
## [19525] 24770 24868 24966 25064 25162 25260 25358 25456 25554 25652 25750 25848
## [19537] 75 173 271 369 467 565 663 761 859 957 1055 1153
## [19549] 1251 1349 1447 1545 1643 1741 1839 1937 2035 2133 2231 2329
## [19561] 2427 2525 2623 2721 2819 2917 3015 3113 3211 3309 3407 3505
## [19573] 3603 3701 3799 3897 3995 4093 4191 4289 4387 4485 4583 4681
## [19585] 4779 4877 4975 5073 5171 5269 5367 5465 5563 5661 5759 5857
## [19597] 5955 6053 6151 6249 6347 6445 6543 6641 6739 6837 6935 7033
## [19609] 7131 7229 7327 7425 7523 7621 7719 7817 7915 8013 8111 8209
## [19621] 8307 8405 8503 8601 8699 8797 8895 8993 9091 9189 9287 9385
## [19633] 9483 9581 9679 9777 9875 9973 10071 10169 10267 10365 10463 10561
## [19645] 10659 10757 10855 10953 11051 11149 11247 11345 11443 11541 11639 11737
## [19657] 11835 11933 12031 12129 12227 12325 12423 12521 12619 12717 12815 12913
## [19669] 13011 13109 13207 13305 13403 13501 13599 13697 13795 13893 13991 14089
## [19681] 14187 14285 14383 14481 14579 14677 14775 14873 14971 15069 15167 15265
## [19693] 15363 15461 15559 15657 15755 15853 15951 16049 16147 16245 16343 16441
## [19705] 16539 16637 16735 16833 16931 17029 17127 17225 17323 17421 17519 17617
## [19717] 17715 17813 17911 18009 18107 18205 18303 18401 18499 18597 18695 18793
## [19729] 18891 18989 19087 19185 19283 19381 19479 19577 19675 19773 19871 19969
## [19741] 20067 20165 20263 20361 20459 20557 20655 20753 20851 20949 21047 21145
## [19753] 21243 21341 21439 21537 21635 21733 21831 21929 22027 22125 22223 22321
## [19765] 22419 22517 22615 22713 22811 22909 23007 23105 23203 23301 23399 23497
## [19777] 23595 23693 23791 23889 23987 24085 24183 24281 24379 24477 24575 24673
## [19789] 24771 24869 24967 25065 25163 25261 25359 25457 25555 25653 25751 25849
## [19801] 76 174 272 370 468 566 664 762 860 958 1056 1154
## [19813] 1252 1350 1448 1546 1644 1742 1840 1938 2036 2134 2232 2330
## [19825] 2428 2526 2624 2722 2820 2918 3016 3114 3212 3310 3408 3506
## [19837] 3604 3702 3800 3898 3996 4094 4192 4290 4388 4486 4584 4682
## [19849] 4780 4878 4976 5074 5172 5270 5368 5466 5564 5662 5760 5858
## [19861] 5956 6054 6152 6250 6348 6446 6544 6642 6740 6838 6936 7034
## [19873] 7132 7230 7328 7426 7524 7622 7720 7818 7916 8014 8112 8210
## [19885] 8308 8406 8504 8602 8700 8798 8896 8994 9092 9190 9288 9386
## [19897] 9484 9582 9680 9778 9876 9974 10072 10170 10268 10366 10464 10562
## [19909] 10660 10758 10856 10954 11052 11150 11248 11346 11444 11542 11640 11738
## [19921] 11836 11934 12032 12130 12228 12326 12424 12522 12620 12718 12816 12914
## [19933] 13012 13110 13208 13306 13404 13502 13600 13698 13796 13894 13992 14090
## [19945] 14188 14286 14384 14482 14580 14678 14776 14874 14972 15070 15168 15266
## [19957] 15364 15462 15560 15658 15756 15854 15952 16050 16148 16246 16344 16442
## [19969] 16540 16638 16736 16834 16932 17030 17128 17226 17324 17422 17520 17618
## [19981] 17716 17814 17912 18010 18108 18206 18304 18402 18500 18598 18696 18794
## [19993] 18892 18990 19088 19186 19284 19382 19480 19578 19676 19774 19872 19970
## [20005] 20068 20166 20264 20362 20460 20558 20656 20754 20852 20950 21048 21146
## [20017] 21244 21342 21440 21538 21636 21734 21832 21930 22028 22126 22224 22322
## [20029] 22420 22518 22616 22714 22812 22910 23008 23106 23204 23302 23400 23498
## [20041] 23596 23694 23792 23890 23988 24086 24184 24282 24380 24478 24576 24674
## [20053] 24772 24870 24968 25066 25164 25262 25360 25458 25556 25654 25752 25850
## [20065] 77 175 273 371 469 567 665 763 861 959 1057 1155
## [20077] 1253 1351 1449 1547 1645 1743 1841 1939 2037 2135 2233 2331
## [20089] 2429 2527 2625 2723 2821 2919 3017 3115 3213 3311 3409 3507
## [20101] 3605 3703 3801 3899 3997 4095 4193 4291 4389 4487 4585 4683
## [20113] 4781 4879 4977 5075 5173 5271 5369 5467 5565 5663 5761 5859
## [20125] 5957 6055 6153 6251 6349 6447 6545 6643 6741 6839 6937 7035
## [20137] 7133 7231 7329 7427 7525 7623 7721 7819 7917 8015 8113 8211
## [20149] 8309 8407 8505 8603 8701 8799 8897 8995 9093 9191 9289 9387
## [20161] 9485 9583 9681 9779 9877 9975 10073 10171 10269 10367 10465 10563
## [20173] 10661 10759 10857 10955 11053 11151 11249 11347 11445 11543 11641 11739
## [20185] 11837 11935 12033 12131 12229 12327 12425 12523 12621 12719 12817 12915
## [20197] 13013 13111 13209 13307 13405 13503 13601 13699 13797 13895 13993 14091
## [20209] 14189 14287 14385 14483 14581 14679 14777 14875 14973 15071 15169 15267
## [20221] 15365 15463 15561 15659 15757 15855 15953 16051 16149 16247 16345 16443
## [20233] 16541 16639 16737 16835 16933 17031 17129 17227 17325 17423 17521 17619
## [20245] 17717 17815 17913 18011 18109 18207 18305 18403 18501 18599 18697 18795
## [20257] 18893 18991 19089 19187 19285 19383 19481 19579 19677 19775 19873 19971
## [20269] 20069 20167 20265 20363 20461 20559 20657 20755 20853 20951 21049 21147
## [20281] 21245 21343 21441 21539 21637 21735 21833 21931 22029 22127 22225 22323
## [20293] 22421 22519 22617 22715 22813 22911 23009 23107 23205 23303 23401 23499
## [20305] 23597 23695 23793 23891 23989 24087 24185 24283 24381 24479 24577 24675
## [20317] 24773 24871 24969 25067 25165 25263 25361 25459 25557 25655 25753 25851
## [20329] 78 176 274 372 470 568 666 764 862 960 1058 1156
## [20341] 1254 1352 1450 1548 1646 1744 1842 1940 2038 2136 2234 2332
## [20353] 2430 2528 2626 2724 2822 2920 3018 3116 3214 3312 3410 3508
## [20365] 3606 3704 3802 3900 3998 4096 4194 4292 4390 4488 4586 4684
## [20377] 4782 4880 4978 5076 5174 5272 5370 5468 5566 5664 5762 5860
## [20389] 5958 6056 6154 6252 6350 6448 6546 6644 6742 6840 6938 7036
## [20401] 7134 7232 7330 7428 7526 7624 7722 7820 7918 8016 8114 8212
## [20413] 8310 8408 8506 8604 8702 8800 8898 8996 9094 9192 9290 9388
## [20425] 9486 9584 9682 9780 9878 9976 10074 10172 10270 10368 10466 10564
## [20437] 10662 10760 10858 10956 11054 11152 11250 11348 11446 11544 11642 11740
## [20449] 11838 11936 12034 12132 12230 12328 12426 12524 12622 12720 12818 12916
## [20461] 13014 13112 13210 13308 13406 13504 13602 13700 13798 13896 13994 14092
## [20473] 14190 14288 14386 14484 14582 14680 14778 14876 14974 15072 15170 15268
## [20485] 15366 15464 15562 15660 15758 15856 15954 16052 16150 16248 16346 16444
## [20497] 16542 16640 16738 16836 16934 17032 17130 17228 17326 17424 17522 17620
## [20509] 17718 17816 17914 18012 18110 18208 18306 18404 18502 18600 18698 18796
## [20521] 18894 18992 19090 19188 19286 19384 19482 19580 19678 19776 19874 19972
## [20533] 20070 20168 20266 20364 20462 20560 20658 20756 20854 20952 21050 21148
## [20545] 21246 21344 21442 21540 21638 21736 21834 21932 22030 22128 22226 22324
## [20557] 22422 22520 22618 22716 22814 22912 23010 23108 23206 23304 23402 23500
## [20569] 23598 23696 23794 23892 23990 24088 24186 24284 24382 24480 24578 24676
## [20581] 24774 24872 24970 25068 25166 25264 25362 25460 25558 25656 25754 25852
## [20593] 79 177 275 373 471 569 667 765 863 961 1059 1157
## [20605] 1255 1353 1451 1549 1647 1745 1843 1941 2039 2137 2235 2333
## [20617] 2431 2529 2627 2725 2823 2921 3019 3117 3215 3313 3411 3509
## [20629] 3607 3705 3803 3901 3999 4097 4195 4293 4391 4489 4587 4685
## [20641] 4783 4881 4979 5077 5175 5273 5371 5469 5567 5665 5763 5861
## [20653] 5959 6057 6155 6253 6351 6449 6547 6645 6743 6841 6939 7037
## [20665] 7135 7233 7331 7429 7527 7625 7723 7821 7919 8017 8115 8213
## [20677] 8311 8409 8507 8605 8703 8801 8899 8997 9095 9193 9291 9389
## [20689] 9487 9585 9683 9781 9879 9977 10075 10173 10271 10369 10467 10565
## [20701] 10663 10761 10859 10957 11055 11153 11251 11349 11447 11545 11643 11741
## [20713] 11839 11937 12035 12133 12231 12329 12427 12525 12623 12721 12819 12917
## [20725] 13015 13113 13211 13309 13407 13505 13603 13701 13799 13897 13995 14093
## [20737] 14191 14289 14387 14485 14583 14681 14779 14877 14975 15073 15171 15269
## [20749] 15367 15465 15563 15661 15759 15857 15955 16053 16151 16249 16347 16445
## [20761] 16543 16641 16739 16837 16935 17033 17131 17229 17327 17425 17523 17621
## [20773] 17719 17817 17915 18013 18111 18209 18307 18405 18503 18601 18699 18797
## [20785] 18895 18993 19091 19189 19287 19385 19483 19581 19679 19777 19875 19973
## [20797] 20071 20169 20267 20365 20463 20561 20659 20757 20855 20953 21051 21149
## [20809] 21247 21345 21443 21541 21639 21737 21835 21933 22031 22129 22227 22325
## [20821] 22423 22521 22619 22717 22815 22913 23011 23109 23207 23305 23403 23501
## [20833] 23599 23697 23795 23893 23991 24089 24187 24285 24383 24481 24579 24677
## [20845] 24775 24873 24971 25069 25167 25265 25363 25461 25559 25657 25755 25853
## [20857] 80 178 276 374 472 570 668 766 864 962 1060 1158
## [20869] 1256 1354 1452 1550 1648 1746 1844 1942 2040 2138 2236 2334
## [20881] 2432 2530 2628 2726 2824 2922 3020 3118 3216 3314 3412 3510
## [20893] 3608 3706 3804 3902 4000 4098 4196 4294 4392 4490 4588 4686
## [20905] 4784 4882 4980 5078 5176 5274 5372 5470 5568 5666 5764 5862
## [20917] 5960 6058 6156 6254 6352 6450 6548 6646 6744 6842 6940 7038
## [20929] 7136 7234 7332 7430 7528 7626 7724 7822 7920 8018 8116 8214
## [20941] 8312 8410 8508 8606 8704 8802 8900 8998 9096 9194 9292 9390
## [20953] 9488 9586 9684 9782 9880 9978 10076 10174 10272 10370 10468 10566
## [20965] 10664 10762 10860 10958 11056 11154 11252 11350 11448 11546 11644 11742
## [20977] 11840 11938 12036 12134 12232 12330 12428 12526 12624 12722 12820 12918
## [20989] 13016 13114 13212 13310 13408 13506 13604 13702 13800 13898 13996 14094
## [21001] 14192 14290 14388 14486 14584 14682 14780 14878 14976 15074 15172 15270
## [21013] 15368 15466 15564 15662 15760 15858 15956 16054 16152 16250 16348 16446
## [21025] 16544 16642 16740 16838 16936 17034 17132 17230 17328 17426 17524 17622
## [21037] 17720 17818 17916 18014 18112 18210 18308 18406 18504 18602 18700 18798
## [21049] 18896 18994 19092 19190 19288 19386 19484 19582 19680 19778 19876 19974
## [21061] 20072 20170 20268 20366 20464 20562 20660 20758 20856 20954 21052 21150
## [21073] 21248 21346 21444 21542 21640 21738 21836 21934 22032 22130 22228 22326
## [21085] 22424 22522 22620 22718 22816 22914 23012 23110 23208 23306 23404 23502
## [21097] 23600 23698 23796 23894 23992 24090 24188 24286 24384 24482 24580 24678
## [21109] 24776 24874 24972 25070 25168 25266 25364 25462 25560 25658 25756 25854
## [21121] 81 179 277 375 473 571 669 767 865 963 1061 1159
## [21133] 1257 1355 1453 1551 1649 1747 1845 1943 2041 2139 2237 2335
## [21145] 2433 2531 2629 2727 2825 2923 3021 3119 3217 3315 3413 3511
## [21157] 3609 3707 3805 3903 4001 4099 4197 4295 4393 4491 4589 4687
## [21169] 4785 4883 4981 5079 5177 5275 5373 5471 5569 5667 5765 5863
## [21181] 5961 6059 6157 6255 6353 6451 6549 6647 6745 6843 6941 7039
## [21193] 7137 7235 7333 7431 7529 7627 7725 7823 7921 8019 8117 8215
## [21205] 8313 8411 8509 8607 8705 8803 8901 8999 9097 9195 9293 9391
## [21217] 9489 9587 9685 9783 9881 9979 10077 10175 10273 10371 10469 10567
## [21229] 10665 10763 10861 10959 11057 11155 11253 11351 11449 11547 11645 11743
## [21241] 11841 11939 12037 12135 12233 12331 12429 12527 12625 12723 12821 12919
## [21253] 13017 13115 13213 13311 13409 13507 13605 13703 13801 13899 13997 14095
## [21265] 14193 14291 14389 14487 14585 14683 14781 14879 14977 15075 15173 15271
## [21277] 15369 15467 15565 15663 15761 15859 15957 16055 16153 16251 16349 16447
## [21289] 16545 16643 16741 16839 16937 17035 17133 17231 17329 17427 17525 17623
## [21301] 17721 17819 17917 18015 18113 18211 18309 18407 18505 18603 18701 18799
## [21313] 18897 18995 19093 19191 19289 19387 19485 19583 19681 19779 19877 19975
## [21325] 20073 20171 20269 20367 20465 20563 20661 20759 20857 20955 21053 21151
## [21337] 21249 21347 21445 21543 21641 21739 21837 21935 22033 22131 22229 22327
## [21349] 22425 22523 22621 22719 22817 22915 23013 23111 23209 23307 23405 23503
## [21361] 23601 23699 23797 23895 23993 24091 24189 24287 24385 24483 24581 24679
## [21373] 24777 24875 24973 25071 25169 25267 25365 25463 25561 25659 25757 25855
## [21385] 82 180 278 376 474 572 670 768 866 964 1062 1160
## [21397] 1258 1356 1454 1552 1650 1748 1846 1944 2042 2140 2238 2336
## [21409] 2434 2532 2630 2728 2826 2924 3022 3120 3218 3316 3414 3512
## [21421] 3610 3708 3806 3904 4002 4100 4198 4296 4394 4492 4590 4688
## [21433] 4786 4884 4982 5080 5178 5276 5374 5472 5570 5668 5766 5864
## [21445] 5962 6060 6158 6256 6354 6452 6550 6648 6746 6844 6942 7040
## [21457] 7138 7236 7334 7432 7530 7628 7726 7824 7922 8020 8118 8216
## [21469] 8314 8412 8510 8608 8706 8804 8902 9000 9098 9196 9294 9392
## [21481] 9490 9588 9686 9784 9882 9980 10078 10176 10274 10372 10470 10568
## [21493] 10666 10764 10862 10960 11058 11156 11254 11352 11450 11548 11646 11744
## [21505] 11842 11940 12038 12136 12234 12332 12430 12528 12626 12724 12822 12920
## [21517] 13018 13116 13214 13312 13410 13508 13606 13704 13802 13900 13998 14096
## [21529] 14194 14292 14390 14488 14586 14684 14782 14880 14978 15076 15174 15272
## [21541] 15370 15468 15566 15664 15762 15860 15958 16056 16154 16252 16350 16448
## [21553] 16546 16644 16742 16840 16938 17036 17134 17232 17330 17428 17526 17624
## [21565] 17722 17820 17918 18016 18114 18212 18310 18408 18506 18604 18702 18800
## [21577] 18898 18996 19094 19192 19290 19388 19486 19584 19682 19780 19878 19976
## [21589] 20074 20172 20270 20368 20466 20564 20662 20760 20858 20956 21054 21152
## [21601] 21250 21348 21446 21544 21642 21740 21838 21936 22034 22132 22230 22328
## [21613] 22426 22524 22622 22720 22818 22916 23014 23112 23210 23308 23406 23504
## [21625] 23602 23700 23798 23896 23994 24092 24190 24288 24386 24484 24582 24680
## [21637] 24778 24876 24974 25072 25170 25268 25366 25464 25562 25660 25758 25856
## [21649] 83 181 279 377 475 573 671 769 867 965 1063 1161
## [21661] 1259 1357 1455 1553 1651 1749 1847 1945 2043 2141 2239 2337
## [21673] 2435 2533 2631 2729 2827 2925 3023 3121 3219 3317 3415 3513
## [21685] 3611 3709 3807 3905 4003 4101 4199 4297 4395 4493 4591 4689
## [21697] 4787 4885 4983 5081 5179 5277 5375 5473 5571 5669 5767 5865
## [21709] 5963 6061 6159 6257 6355 6453 6551 6649 6747 6845 6943 7041
## [21721] 7139 7237 7335 7433 7531 7629 7727 7825 7923 8021 8119 8217
## [21733] 8315 8413 8511 8609 8707 8805 8903 9001 9099 9197 9295 9393
## [21745] 9491 9589 9687 9785 9883 9981 10079 10177 10275 10373 10471 10569
## [21757] 10667 10765 10863 10961 11059 11157 11255 11353 11451 11549 11647 11745
## [21769] 11843 11941 12039 12137 12235 12333 12431 12529 12627 12725 12823 12921
## [21781] 13019 13117 13215 13313 13411 13509 13607 13705 13803 13901 13999 14097
## [21793] 14195 14293 14391 14489 14587 14685 14783 14881 14979 15077 15175 15273
## [21805] 15371 15469 15567 15665 15763 15861 15959 16057 16155 16253 16351 16449
## [21817] 16547 16645 16743 16841 16939 17037 17135 17233 17331 17429 17527 17625
## [21829] 17723 17821 17919 18017 18115 18213 18311 18409 18507 18605 18703 18801
## [21841] 18899 18997 19095 19193 19291 19389 19487 19585 19683 19781 19879 19977
## [21853] 20075 20173 20271 20369 20467 20565 20663 20761 20859 20957 21055 21153
## [21865] 21251 21349 21447 21545 21643 21741 21839 21937 22035 22133 22231 22329
## [21877] 22427 22525 22623 22721 22819 22917 23015 23113 23211 23309 23407 23505
## [21889] 23603 23701 23799 23897 23995 24093 24191 24289 24387 24485 24583 24681
## [21901] 24779 24877 24975 25073 25171 25269 25367 25465 25563 25661 25759 25857
## [21913] 84 182 280 378 476 574 672 770 868 966 1064 1162
## [21925] 1260 1358 1456 1554 1652 1750 1848 1946 2044 2142 2240 2338
## [21937] 2436 2534 2632 2730 2828 2926 3024 3122 3220 3318 3416 3514
## [21949] 3612 3710 3808 3906 4004 4102 4200 4298 4396 4494 4592 4690
## [21961] 4788 4886 4984 5082 5180 5278 5376 5474 5572 5670 5768 5866
## [21973] 5964 6062 6160 6258 6356 6454 6552 6650 6748 6846 6944 7042
## [21985] 7140 7238 7336 7434 7532 7630 7728 7826 7924 8022 8120 8218
## [21997] 8316 8414 8512 8610 8708 8806 8904 9002 9100 9198 9296 9394
## [22009] 9492 9590 9688 9786 9884 9982 10080 10178 10276 10374 10472 10570
## [22021] 10668 10766 10864 10962 11060 11158 11256 11354 11452 11550 11648 11746
## [22033] 11844 11942 12040 12138 12236 12334 12432 12530 12628 12726 12824 12922
## [22045] 13020 13118 13216 13314 13412 13510 13608 13706 13804 13902 14000 14098
## [22057] 14196 14294 14392 14490 14588 14686 14784 14882 14980 15078 15176 15274
## [22069] 15372 15470 15568 15666 15764 15862 15960 16058 16156 16254 16352 16450
## [22081] 16548 16646 16744 16842 16940 17038 17136 17234 17332 17430 17528 17626
## [22093] 17724 17822 17920 18018 18116 18214 18312 18410 18508 18606 18704 18802
## [22105] 18900 18998 19096 19194 19292 19390 19488 19586 19684 19782 19880 19978
## [22117] 20076 20174 20272 20370 20468 20566 20664 20762 20860 20958 21056 21154
## [22129] 21252 21350 21448 21546 21644 21742 21840 21938 22036 22134 22232 22330
## [22141] 22428 22526 22624 22722 22820 22918 23016 23114 23212 23310 23408 23506
## [22153] 23604 23702 23800 23898 23996 24094 24192 24290 24388 24486 24584 24682
## [22165] 24780 24878 24976 25074 25172 25270 25368 25466 25564 25662 25760 25858
## [22177] 85 183 281 379 477 575 673 771 869 967 1065 1163
## [22189] 1261 1359 1457 1555 1653 1751 1849 1947 2045 2143 2241 2339
## [22201] 2437 2535 2633 2731 2829 2927 3025 3123 3221 3319 3417 3515
## [22213] 3613 3711 3809 3907 4005 4103 4201 4299 4397 4495 4593 4691
## [22225] 4789 4887 4985 5083 5181 5279 5377 5475 5573 5671 5769 5867
## [22237] 5965 6063 6161 6259 6357 6455 6553 6651 6749 6847 6945 7043
## [22249] 7141 7239 7337 7435 7533 7631 7729 7827 7925 8023 8121 8219
## [22261] 8317 8415 8513 8611 8709 8807 8905 9003 9101 9199 9297 9395
## [22273] 9493 9591 9689 9787 9885 9983 10081 10179 10277 10375 10473 10571
## [22285] 10669 10767 10865 10963 11061 11159 11257 11355 11453 11551 11649 11747
## [22297] 11845 11943 12041 12139 12237 12335 12433 12531 12629 12727 12825 12923
## [22309] 13021 13119 13217 13315 13413 13511 13609 13707 13805 13903 14001 14099
## [22321] 14197 14295 14393 14491 14589 14687 14785 14883 14981 15079 15177 15275
## [22333] 15373 15471 15569 15667 15765 15863 15961 16059 16157 16255 16353 16451
## [22345] 16549 16647 16745 16843 16941 17039 17137 17235 17333 17431 17529 17627
## [22357] 17725 17823 17921 18019 18117 18215 18313 18411 18509 18607 18705 18803
## [22369] 18901 18999 19097 19195 19293 19391 19489 19587 19685 19783 19881 19979
## [22381] 20077 20175 20273 20371 20469 20567 20665 20763 20861 20959 21057 21155
## [22393] 21253 21351 21449 21547 21645 21743 21841 21939 22037 22135 22233 22331
## [22405] 22429 22527 22625 22723 22821 22919 23017 23115 23213 23311 23409 23507
## [22417] 23605 23703 23801 23899 23997 24095 24193 24291 24389 24487 24585 24683
## [22429] 24781 24879 24977 25075 25173 25271 25369 25467 25565 25663 25761 25859
## [22441] 86 184 282 380 478 576 674 772 870 968 1066 1164
## [22453] 1262 1360 1458 1556 1654 1752 1850 1948 2046 2144 2242 2340
## [22465] 2438 2536 2634 2732 2830 2928 3026 3124 3222 3320 3418 3516
## [22477] 3614 3712 3810 3908 4006 4104 4202 4300 4398 4496 4594 4692
## [22489] 4790 4888 4986 5084 5182 5280 5378 5476 5574 5672 5770 5868
## [22501] 5966 6064 6162 6260 6358 6456 6554 6652 6750 6848 6946 7044
## [22513] 7142 7240 7338 7436 7534 7632 7730 7828 7926 8024 8122 8220
## [22525] 8318 8416 8514 8612 8710 8808 8906 9004 9102 9200 9298 9396
## [22537] 9494 9592 9690 9788 9886 9984 10082 10180 10278 10376 10474 10572
## [22549] 10670 10768 10866 10964 11062 11160 11258 11356 11454 11552 11650 11748
## [22561] 11846 11944 12042 12140 12238 12336 12434 12532 12630 12728 12826 12924
## [22573] 13022 13120 13218 13316 13414 13512 13610 13708 13806 13904 14002 14100
## [22585] 14198 14296 14394 14492 14590 14688 14786 14884 14982 15080 15178 15276
## [22597] 15374 15472 15570 15668 15766 15864 15962 16060 16158 16256 16354 16452
## [22609] 16550 16648 16746 16844 16942 17040 17138 17236 17334 17432 17530 17628
## [22621] 17726 17824 17922 18020 18118 18216 18314 18412 18510 18608 18706 18804
## [22633] 18902 19000 19098 19196 19294 19392 19490 19588 19686 19784 19882 19980
## [22645] 20078 20176 20274 20372 20470 20568 20666 20764 20862 20960 21058 21156
## [22657] 21254 21352 21450 21548 21646 21744 21842 21940 22038 22136 22234 22332
## [22669] 22430 22528 22626 22724 22822 22920 23018 23116 23214 23312 23410 23508
## [22681] 23606 23704 23802 23900 23998 24096 24194 24292 24390 24488 24586 24684
## [22693] 24782 24880 24978 25076 25174 25272 25370 25468 25566 25664 25762 25860
## [22705] 87 185 283 381 479 577 675 773 871 969 1067 1165
## [22717] 1263 1361 1459 1557 1655 1753 1851 1949 2047 2145 2243 2341
## [22729] 2439 2537 2635 2733 2831 2929 3027 3125 3223 3321 3419 3517
## [22741] 3615 3713 3811 3909 4007 4105 4203 4301 4399 4497 4595 4693
## [22753] 4791 4889 4987 5085 5183 5281 5379 5477 5575 5673 5771 5869
## [22765] 5967 6065 6163 6261 6359 6457 6555 6653 6751 6849 6947 7045
## [22777] 7143 7241 7339 7437 7535 7633 7731 7829 7927 8025 8123 8221
## [22789] 8319 8417 8515 8613 8711 8809 8907 9005 9103 9201 9299 9397
## [22801] 9495 9593 9691 9789 9887 9985 10083 10181 10279 10377 10475 10573
## [22813] 10671 10769 10867 10965 11063 11161 11259 11357 11455 11553 11651 11749
## [22825] 11847 11945 12043 12141 12239 12337 12435 12533 12631 12729 12827 12925
## [22837] 13023 13121 13219 13317 13415 13513 13611 13709 13807 13905 14003 14101
## [22849] 14199 14297 14395 14493 14591 14689 14787 14885 14983 15081 15179 15277
## [22861] 15375 15473 15571 15669 15767 15865 15963 16061 16159 16257 16355 16453
## [22873] 16551 16649 16747 16845 16943 17041 17139 17237 17335 17433 17531 17629
## [22885] 17727 17825 17923 18021 18119 18217 18315 18413 18511 18609 18707 18805
## [22897] 18903 19001 19099 19197 19295 19393 19491 19589 19687 19785 19883 19981
## [22909] 20079 20177 20275 20373 20471 20569 20667 20765 20863 20961 21059 21157
## [22921] 21255 21353 21451 21549 21647 21745 21843 21941 22039 22137 22235 22333
## [22933] 22431 22529 22627 22725 22823 22921 23019 23117 23215 23313 23411 23509
## [22945] 23607 23705 23803 23901 23999 24097 24195 24293 24391 24489 24587 24685
## [22957] 24783 24881 24979 25077 25175 25273 25371 25469 25567 25665 25763 25861
## [22969] 88 186 284 382 480 578 676 774 872 970 1068 1166
## [22981] 1264 1362 1460 1558 1656 1754 1852 1950 2048 2146 2244 2342
## [22993] 2440 2538 2636 2734 2832 2930 3028 3126 3224 3322 3420 3518
## [23005] 3616 3714 3812 3910 4008 4106 4204 4302 4400 4498 4596 4694
## [23017] 4792 4890 4988 5086 5184 5282 5380 5478 5576 5674 5772 5870
## [23029] 5968 6066 6164 6262 6360 6458 6556 6654 6752 6850 6948 7046
## [23041] 7144 7242 7340 7438 7536 7634 7732 7830 7928 8026 8124 8222
## [23053] 8320 8418 8516 8614 8712 8810 8908 9006 9104 9202 9300 9398
## [23065] 9496 9594 9692 9790 9888 9986 10084 10182 10280 10378 10476 10574
## [23077] 10672 10770 10868 10966 11064 11162 11260 11358 11456 11554 11652 11750
## [23089] 11848 11946 12044 12142 12240 12338 12436 12534 12632 12730 12828 12926
## [23101] 13024 13122 13220 13318 13416 13514 13612 13710 13808 13906 14004 14102
## [23113] 14200 14298 14396 14494 14592 14690 14788 14886 14984 15082 15180 15278
## [23125] 15376 15474 15572 15670 15768 15866 15964 16062 16160 16258 16356 16454
## [23137] 16552 16650 16748 16846 16944 17042 17140 17238 17336 17434 17532 17630
## [23149] 17728 17826 17924 18022 18120 18218 18316 18414 18512 18610 18708 18806
## [23161] 18904 19002 19100 19198 19296 19394 19492 19590 19688 19786 19884 19982
## [23173] 20080 20178 20276 20374 20472 20570 20668 20766 20864 20962 21060 21158
## [23185] 21256 21354 21452 21550 21648 21746 21844 21942 22040 22138 22236 22334
## [23197] 22432 22530 22628 22726 22824 22922 23020 23118 23216 23314 23412 23510
## [23209] 23608 23706 23804 23902 24000 24098 24196 24294 24392 24490 24588 24686
## [23221] 24784 24882 24980 25078 25176 25274 25372 25470 25568 25666 25764 25862
## [23233] 89 187 285 383 481 579 677 775 873 971 1069 1167
## [23245] 1265 1363 1461 1559 1657 1755 1853 1951 2049 2147 2245 2343
## [23257] 2441 2539 2637 2735 2833 2931 3029 3127 3225 3323 3421 3519
## [23269] 3617 3715 3813 3911 4009 4107 4205 4303 4401 4499 4597 4695
## [23281] 4793 4891 4989 5087 5185 5283 5381 5479 5577 5675 5773 5871
## [23293] 5969 6067 6165 6263 6361 6459 6557 6655 6753 6851 6949 7047
## [23305] 7145 7243 7341 7439 7537 7635 7733 7831 7929 8027 8125 8223
## [23317] 8321 8419 8517 8615 8713 8811 8909 9007 9105 9203 9301 9399
## [23329] 9497 9595 9693 9791 9889 9987 10085 10183 10281 10379 10477 10575
## [23341] 10673 10771 10869 10967 11065 11163 11261 11359 11457 11555 11653 11751
## [23353] 11849 11947 12045 12143 12241 12339 12437 12535 12633 12731 12829 12927
## [23365] 13025 13123 13221 13319 13417 13515 13613 13711 13809 13907 14005 14103
## [23377] 14201 14299 14397 14495 14593 14691 14789 14887 14985 15083 15181 15279
## [23389] 15377 15475 15573 15671 15769 15867 15965 16063 16161 16259 16357 16455
## [23401] 16553 16651 16749 16847 16945 17043 17141 17239 17337 17435 17533 17631
## [23413] 17729 17827 17925 18023 18121 18219 18317 18415 18513 18611 18709 18807
## [23425] 18905 19003 19101 19199 19297 19395 19493 19591 19689 19787 19885 19983
## [23437] 20081 20179 20277 20375 20473 20571 20669 20767 20865 20963 21061 21159
## [23449] 21257 21355 21453 21551 21649 21747 21845 21943 22041 22139 22237 22335
## [23461] 22433 22531 22629 22727 22825 22923 23021 23119 23217 23315 23413 23511
## [23473] 23609 23707 23805 23903 24001 24099 24197 24295 24393 24491 24589 24687
## [23485] 24785 24883 24981 25079 25177 25275 25373 25471 25569 25667 25765 25863
## [23497] 90 188 286 384 482 580 678 776 874 972 1070 1168
## [23509] 1266 1364 1462 1560 1658 1756 1854 1952 2050 2148 2246 2344
## [23521] 2442 2540 2638 2736 2834 2932 3030 3128 3226 3324 3422 3520
## [23533] 3618 3716 3814 3912 4010 4108 4206 4304 4402 4500 4598 4696
## [23545] 4794 4892 4990 5088 5186 5284 5382 5480 5578 5676 5774 5872
## [23557] 5970 6068 6166 6264 6362 6460 6558 6656 6754 6852 6950 7048
## [23569] 7146 7244 7342 7440 7538 7636 7734 7832 7930 8028 8126 8224
## [23581] 8322 8420 8518 8616 8714 8812 8910 9008 9106 9204 9302 9400
## [23593] 9498 9596 9694 9792 9890 9988 10086 10184 10282 10380 10478 10576
## [23605] 10674 10772 10870 10968 11066 11164 11262 11360 11458 11556 11654 11752
## [23617] 11850 11948 12046 12144 12242 12340 12438 12536 12634 12732 12830 12928
## [23629] 13026 13124 13222 13320 13418 13516 13614 13712 13810 13908 14006 14104
## [23641] 14202 14300 14398 14496 14594 14692 14790 14888 14986 15084 15182 15280
## [23653] 15378 15476 15574 15672 15770 15868 15966 16064 16162 16260 16358 16456
## [23665] 16554 16652 16750 16848 16946 17044 17142 17240 17338 17436 17534 17632
## [23677] 17730 17828 17926 18024 18122 18220 18318 18416 18514 18612 18710 18808
## [23689] 18906 19004 19102 19200 19298 19396 19494 19592 19690 19788 19886 19984
## [23701] 20082 20180 20278 20376 20474 20572 20670 20768 20866 20964 21062 21160
## [23713] 21258 21356 21454 21552 21650 21748 21846 21944 22042 22140 22238 22336
## [23725] 22434 22532 22630 22728 22826 22924 23022 23120 23218 23316 23414 23512
## [23737] 23610 23708 23806 23904 24002 24100 24198 24296 24394 24492 24590 24688
## [23749] 24786 24884 24982 25080 25178 25276 25374 25472 25570 25668 25766 25864
## [23761] 91 189 287 385 483 581 679 777 875 973 1071 1169
## [23773] 1267 1365 1463 1561 1659 1757 1855 1953 2051 2149 2247 2345
## [23785] 2443 2541 2639 2737 2835 2933 3031 3129 3227 3325 3423 3521
## [23797] 3619 3717 3815 3913 4011 4109 4207 4305 4403 4501 4599 4697
## [23809] 4795 4893 4991 5089 5187 5285 5383 5481 5579 5677 5775 5873
## [23821] 5971 6069 6167 6265 6363 6461 6559 6657 6755 6853 6951 7049
## [23833] 7147 7245 7343 7441 7539 7637 7735 7833 7931 8029 8127 8225
## [23845] 8323 8421 8519 8617 8715 8813 8911 9009 9107 9205 9303 9401
## [23857] 9499 9597 9695 9793 9891 9989 10087 10185 10283 10381 10479 10577
## [23869] 10675 10773 10871 10969 11067 11165 11263 11361 11459 11557 11655 11753
## [23881] 11851 11949 12047 12145 12243 12341 12439 12537 12635 12733 12831 12929
## [23893] 13027 13125 13223 13321 13419 13517 13615 13713 13811 13909 14007 14105
## [23905] 14203 14301 14399 14497 14595 14693 14791 14889 14987 15085 15183 15281
## [23917] 15379 15477 15575 15673 15771 15869 15967 16065 16163 16261 16359 16457
## [23929] 16555 16653 16751 16849 16947 17045 17143 17241 17339 17437 17535 17633
## [23941] 17731 17829 17927 18025 18123 18221 18319 18417 18515 18613 18711 18809
## [23953] 18907 19005 19103 19201 19299 19397 19495 19593 19691 19789 19887 19985
## [23965] 20083 20181 20279 20377 20475 20573 20671 20769 20867 20965 21063 21161
## [23977] 21259 21357 21455 21553 21651 21749 21847 21945 22043 22141 22239 22337
## [23989] 22435 22533 22631 22729 22827 22925 23023 23121 23219 23317 23415 23513
## [24001] 23611 23709 23807 23905 24003 24101 24199 24297 24395 24493 24591 24689
## [24013] 24787 24885 24983 25081 25179 25277 25375 25473 25571 25669 25767 25865
## [24025] 92 190 288 386 484 582 680 778 876 974 1072 1170
## [24037] 1268 1366 1464 1562 1660 1758 1856 1954 2052 2150 2248 2346
## [24049] 2444 2542 2640 2738 2836 2934 3032 3130 3228 3326 3424 3522
## [24061] 3620 3718 3816 3914 4012 4110 4208 4306 4404 4502 4600 4698
## [24073] 4796 4894 4992 5090 5188 5286 5384 5482 5580 5678 5776 5874
## [24085] 5972 6070 6168 6266 6364 6462 6560 6658 6756 6854 6952 7050
## [24097] 7148 7246 7344 7442 7540 7638 7736 7834 7932 8030 8128 8226
## [24109] 8324 8422 8520 8618 8716 8814 8912 9010 9108 9206 9304 9402
## [24121] 9500 9598 9696 9794 9892 9990 10088 10186 10284 10382 10480 10578
## [24133] 10676 10774 10872 10970 11068 11166 11264 11362 11460 11558 11656 11754
## [24145] 11852 11950 12048 12146 12244 12342 12440 12538 12636 12734 12832 12930
## [24157] 13028 13126 13224 13322 13420 13518 13616 13714 13812 13910 14008 14106
## [24169] 14204 14302 14400 14498 14596 14694 14792 14890 14988 15086 15184 15282
## [24181] 15380 15478 15576 15674 15772 15870 15968 16066 16164 16262 16360 16458
## [24193] 16556 16654 16752 16850 16948 17046 17144 17242 17340 17438 17536 17634
## [24205] 17732 17830 17928 18026 18124 18222 18320 18418 18516 18614 18712 18810
## [24217] 18908 19006 19104 19202 19300 19398 19496 19594 19692 19790 19888 19986
## [24229] 20084 20182 20280 20378 20476 20574 20672 20770 20868 20966 21064 21162
## [24241] 21260 21358 21456 21554 21652 21750 21848 21946 22044 22142 22240 22338
## [24253] 22436 22534 22632 22730 22828 22926 23024 23122 23220 23318 23416 23514
## [24265] 23612 23710 23808 23906 24004 24102 24200 24298 24396 24494 24592 24690
## [24277] 24788 24886 24984 25082 25180 25278 25376 25474 25572 25670 25768 25866
## [24289] 93 191 289 387 485 583 681 779 877 975 1073 1171
## [24301] 1269 1367 1465 1563 1661 1759 1857 1955 2053 2151 2249 2347
## [24313] 2445 2543 2641 2739 2837 2935 3033 3131 3229 3327 3425 3523
## [24325] 3621 3719 3817 3915 4013 4111 4209 4307 4405 4503 4601 4699
## [24337] 4797 4895 4993 5091 5189 5287 5385 5483 5581 5679 5777 5875
## [24349] 5973 6071 6169 6267 6365 6463 6561 6659 6757 6855 6953 7051
## [24361] 7149 7247 7345 7443 7541 7639 7737 7835 7933 8031 8129 8227
## [24373] 8325 8423 8521 8619 8717 8815 8913 9011 9109 9207 9305 9403
## [24385] 9501 9599 9697 9795 9893 9991 10089 10187 10285 10383 10481 10579
## [24397] 10677 10775 10873 10971 11069 11167 11265 11363 11461 11559 11657 11755
## [24409] 11853 11951 12049 12147 12245 12343 12441 12539 12637 12735 12833 12931
## [24421] 13029 13127 13225 13323 13421 13519 13617 13715 13813 13911 14009 14107
## [24433] 14205 14303 14401 14499 14597 14695 14793 14891 14989 15087 15185 15283
## [24445] 15381 15479 15577 15675 15773 15871 15969 16067 16165 16263 16361 16459
## [24457] 16557 16655 16753 16851 16949 17047 17145 17243 17341 17439 17537 17635
## [24469] 17733 17831 17929 18027 18125 18223 18321 18419 18517 18615 18713 18811
## [24481] 18909 19007 19105 19203 19301 19399 19497 19595 19693 19791 19889 19987
## [24493] 20085 20183 20281 20379 20477 20575 20673 20771 20869 20967 21065 21163
## [24505] 21261 21359 21457 21555 21653 21751 21849 21947 22045 22143 22241 22339
## [24517] 22437 22535 22633 22731 22829 22927 23025 23123 23221 23319 23417 23515
## [24529] 23613 23711 23809 23907 24005 24103 24201 24299 24397 24495 24593 24691
## [24541] 24789 24887 24985 25083 25181 25279 25377 25475 25573 25671 25769 25867
## [24553] 94 192 290 388 486 584 682 780 878 976 1074 1172
## [24565] 1270 1368 1466 1564 1662 1760 1858 1956 2054 2152 2250 2348
## [24577] 2446 2544 2642 2740 2838 2936 3034 3132 3230 3328 3426 3524
## [24589] 3622 3720 3818 3916 4014 4112 4210 4308 4406 4504 4602 4700
## [24601] 4798 4896 4994 5092 5190 5288 5386 5484 5582 5680 5778 5876
## [24613] 5974 6072 6170 6268 6366 6464 6562 6660 6758 6856 6954 7052
## [24625] 7150 7248 7346 7444 7542 7640 7738 7836 7934 8032 8130 8228
## [24637] 8326 8424 8522 8620 8718 8816 8914 9012 9110 9208 9306 9404
## [24649] 9502 9600 9698 9796 9894 9992 10090 10188 10286 10384 10482 10580
## [24661] 10678 10776 10874 10972 11070 11168 11266 11364 11462 11560 11658 11756
## [24673] 11854 11952 12050 12148 12246 12344 12442 12540 12638 12736 12834 12932
## [24685] 13030 13128 13226 13324 13422 13520 13618 13716 13814 13912 14010 14108
## [24697] 14206 14304 14402 14500 14598 14696 14794 14892 14990 15088 15186 15284
## [24709] 15382 15480 15578 15676 15774 15872 15970 16068 16166 16264 16362 16460
## [24721] 16558 16656 16754 16852 16950 17048 17146 17244 17342 17440 17538 17636
## [24733] 17734 17832 17930 18028 18126 18224 18322 18420 18518 18616 18714 18812
## [24745] 18910 19008 19106 19204 19302 19400 19498 19596 19694 19792 19890 19988
## [24757] 20086 20184 20282 20380 20478 20576 20674 20772 20870 20968 21066 21164
## [24769] 21262 21360 21458 21556 21654 21752 21850 21948 22046 22144 22242 22340
## [24781] 22438 22536 22634 22732 22830 22928 23026 23124 23222 23320 23418 23516
## [24793] 23614 23712 23810 23908 24006 24104 24202 24300 24398 24496 24594 24692
## [24805] 24790 24888 24986 25084 25182 25280 25378 25476 25574 25672 25770 25868
## [24817] 95 193 291 389 487 585 683 781 879 977 1075 1173
## [24829] 1271 1369 1467 1565 1663 1761 1859 1957 2055 2153 2251 2349
## [24841] 2447 2545 2643 2741 2839 2937 3035 3133 3231 3329 3427 3525
## [24853] 3623 3721 3819 3917 4015 4113 4211 4309 4407 4505 4603 4701
## [24865] 4799 4897 4995 5093 5191 5289 5387 5485 5583 5681 5779 5877
## [24877] 5975 6073 6171 6269 6367 6465 6563 6661 6759 6857 6955 7053
## [24889] 7151 7249 7347 7445 7543 7641 7739 7837 7935 8033 8131 8229
## [24901] 8327 8425 8523 8621 8719 8817 8915 9013 9111 9209 9307 9405
## [24913] 9503 9601 9699 9797 9895 9993 10091 10189 10287 10385 10483 10581
## [24925] 10679 10777 10875 10973 11071 11169 11267 11365 11463 11561 11659 11757
## [24937] 11855 11953 12051 12149 12247 12345 12443 12541 12639 12737 12835 12933
## [24949] 13031 13129 13227 13325 13423 13521 13619 13717 13815 13913 14011 14109
## [24961] 14207 14305 14403 14501 14599 14697 14795 14893 14991 15089 15187 15285
## [24973] 15383 15481 15579 15677 15775 15873 15971 16069 16167 16265 16363 16461
## [24985] 16559 16657 16755 16853 16951 17049 17147 17245 17343 17441 17539 17637
## [24997] 17735 17833 17931 18029 18127 18225 18323 18421 18519 18617 18715 18813
## [25009] 18911 19009 19107 19205 19303 19401 19499 19597 19695 19793 19891 19989
## [25021] 20087 20185 20283 20381 20479 20577 20675 20773 20871 20969 21067 21165
## [25033] 21263 21361 21459 21557 21655 21753 21851 21949 22047 22145 22243 22341
## [25045] 22439 22537 22635 22733 22831 22929 23027 23125 23223 23321 23419 23517
## [25057] 23615 23713 23811 23909 24007 24105 24203 24301 24399 24497 24595 24693
## [25069] 24791 24889 24987 25085 25183 25281 25379 25477 25575 25673 25771 25869
## [25081] 96 194 292 390 488 586 684 782 880 978 1076 1174
## [25093] 1272 1370 1468 1566 1664 1762 1860 1958 2056 2154 2252 2350
## [25105] 2448 2546 2644 2742 2840 2938 3036 3134 3232 3330 3428 3526
## [25117] 3624 3722 3820 3918 4016 4114 4212 4310 4408 4506 4604 4702
## [25129] 4800 4898 4996 5094 5192 5290 5388 5486 5584 5682 5780 5878
## [25141] 5976 6074 6172 6270 6368 6466 6564 6662 6760 6858 6956 7054
## [25153] 7152 7250 7348 7446 7544 7642 7740 7838 7936 8034 8132 8230
## [25165] 8328 8426 8524 8622 8720 8818 8916 9014 9112 9210 9308 9406
## [25177] 9504 9602 9700 9798 9896 9994 10092 10190 10288 10386 10484 10582
## [25189] 10680 10778 10876 10974 11072 11170 11268 11366 11464 11562 11660 11758
## [25201] 11856 11954 12052 12150 12248 12346 12444 12542 12640 12738 12836 12934
## [25213] 13032 13130 13228 13326 13424 13522 13620 13718 13816 13914 14012 14110
## [25225] 14208 14306 14404 14502 14600 14698 14796 14894 14992 15090 15188 15286
## [25237] 15384 15482 15580 15678 15776 15874 15972 16070 16168 16266 16364 16462
## [25249] 16560 16658 16756 16854 16952 17050 17148 17246 17344 17442 17540 17638
## [25261] 17736 17834 17932 18030 18128 18226 18324 18422 18520 18618 18716 18814
## [25273] 18912 19010 19108 19206 19304 19402 19500 19598 19696 19794 19892 19990
## [25285] 20088 20186 20284 20382 20480 20578 20676 20774 20872 20970 21068 21166
## [25297] 21264 21362 21460 21558 21656 21754 21852 21950 22048 22146 22244 22342
## [25309] 22440 22538 22636 22734 22832 22930 23028 23126 23224 23322 23420 23518
## [25321] 23616 23714 23812 23910 24008 24106 24204 24302 24400 24498 24596 24694
## [25333] 24792 24890 24988 25086 25184 25282 25380 25478 25576 25674 25772 25870
## [25345] 97 195 293 391 489 587 685 783 881 979 1077 1175
## [25357] 1273 1371 1469 1567 1665 1763 1861 1959 2057 2155 2253 2351
## [25369] 2449 2547 2645 2743 2841 2939 3037 3135 3233 3331 3429 3527
## [25381] 3625 3723 3821 3919 4017 4115 4213 4311 4409 4507 4605 4703
## [25393] 4801 4899 4997 5095 5193 5291 5389 5487 5585 5683 5781 5879
## [25405] 5977 6075 6173 6271 6369 6467 6565 6663 6761 6859 6957 7055
## [25417] 7153 7251 7349 7447 7545 7643 7741 7839 7937 8035 8133 8231
## [25429] 8329 8427 8525 8623 8721 8819 8917 9015 9113 9211 9309 9407
## [25441] 9505 9603 9701 9799 9897 9995 10093 10191 10289 10387 10485 10583
## [25453] 10681 10779 10877 10975 11073 11171 11269 11367 11465 11563 11661 11759
## [25465] 11857 11955 12053 12151 12249 12347 12445 12543 12641 12739 12837 12935
## [25477] 13033 13131 13229 13327 13425 13523 13621 13719 13817 13915 14013 14111
## [25489] 14209 14307 14405 14503 14601 14699 14797 14895 14993 15091 15189 15287
## [25501] 15385 15483 15581 15679 15777 15875 15973 16071 16169 16267 16365 16463
## [25513] 16561 16659 16757 16855 16953 17051 17149 17247 17345 17443 17541 17639
## [25525] 17737 17835 17933 18031 18129 18227 18325 18423 18521 18619 18717 18815
## [25537] 18913 19011 19109 19207 19305 19403 19501 19599 19697 19795 19893 19991
## [25549] 20089 20187 20285 20383 20481 20579 20677 20775 20873 20971 21069 21167
## [25561] 21265 21363 21461 21559 21657 21755 21853 21951 22049 22147 22245 22343
## [25573] 22441 22539 22637 22735 22833 22931 23029 23127 23225 23323 23421 23519
## [25585] 23617 23715 23813 23911 24009 24107 24205 24303 24401 24499 24597 24695
## [25597] 24793 24891 24989 25087 25185 25283 25381 25479 25577 25675 25773 25871
## [25609] 98 196 294 392 490 588 686 784 882 980 1078 1176
## [25621] 1274 1372 1470 1568 1666 1764 1862 1960 2058 2156 2254 2352
## [25633] 2450 2548 2646 2744 2842 2940 3038 3136 3234 3332 3430 3528
## [25645] 3626 3724 3822 3920 4018 4116 4214 4312 4410 4508 4606 4704
## [25657] 4802 4900 4998 5096 5194 5292 5390 5488 5586 5684 5782 5880
## [25669] 5978 6076 6174 6272 6370 6468 6566 6664 6762 6860 6958 7056
## [25681] 7154 7252 7350 7448 7546 7644 7742 7840 7938 8036 8134 8232
## [25693] 8330 8428 8526 8624 8722 8820 8918 9016 9114 9212 9310 9408
## [25705] 9506 9604 9702 9800 9898 9996 10094 10192 10290 10388 10486 10584
## [25717] 10682 10780 10878 10976 11074 11172 11270 11368 11466 11564 11662 11760
## [25729] 11858 11956 12054 12152 12250 12348 12446 12544 12642 12740 12838 12936
## [25741] 13034 13132 13230 13328 13426 13524 13622 13720 13818 13916 14014 14112
## [25753] 14210 14308 14406 14504 14602 14700 14798 14896 14994 15092 15190 15288
## [25765] 15386 15484 15582 15680 15778 15876 15974 16072 16170 16268 16366 16464
## [25777] 16562 16660 16758 16856 16954 17052 17150 17248 17346 17444 17542 17640
## [25789] 17738 17836 17934 18032 18130 18228 18326 18424 18522 18620 18718 18816
## [25801] 18914 19012 19110 19208 19306 19404 19502 19600 19698 19796 19894 19992
## [25813] 20090 20188 20286 20384 20482 20580 20678 20776 20874 20972 21070 21168
## [25825] 21266 21364 21462 21560 21658 21756 21854 21952 22050 22148 22246 22344
## [25837] 22442 22540 22638 22736 22834 22932 23030 23128 23226 23324 23422 23520
## [25849] 23618 23716 23814 23912 24010 24108 24206 24304 24402 24500 24598 24696
## [25861] 24794 24892 24990 25088 25186 25284 25382 25480 25578 25676 25774 25872
# Now, I repeat these steps for the deceased data.
Initial_Deceased_Wide <- read_csv("data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")
## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## See spec(...) for full column specifications.
# We continue with the reshape.
Deceased_Long <- pivot_longer(Initial_Deceased_Wide, cols = c(5:102), names_to = "Dates", values_to = "Deceased")
require(reshape)
Deceased_Long$COUNTRY <- Deceased_Long$`Country/Region`
Deceased_Long$Date <- mdy(Deceased_Long$Dates, quiet = FALSE, tz = NULL, locale = Sys.getlocale("LC_TIME"),
truncated = 0)
order(Deceased_Long$Date)
## [1] 1 99 197 295 393 491 589 687 785 883 981 1079
## [13] 1177 1275 1373 1471 1569 1667 1765 1863 1961 2059 2157 2255
## [25] 2353 2451 2549 2647 2745 2843 2941 3039 3137 3235 3333 3431
## [37] 3529 3627 3725 3823 3921 4019 4117 4215 4313 4411 4509 4607
## [49] 4705 4803 4901 4999 5097 5195 5293 5391 5489 5587 5685 5783
## [61] 5881 5979 6077 6175 6273 6371 6469 6567 6665 6763 6861 6959
## [73] 7057 7155 7253 7351 7449 7547 7645 7743 7841 7939 8037 8135
## [85] 8233 8331 8429 8527 8625 8723 8821 8919 9017 9115 9213 9311
## [97] 9409 9507 9605 9703 9801 9899 9997 10095 10193 10291 10389 10487
## [109] 10585 10683 10781 10879 10977 11075 11173 11271 11369 11467 11565 11663
## [121] 11761 11859 11957 12055 12153 12251 12349 12447 12545 12643 12741 12839
## [133] 12937 13035 13133 13231 13329 13427 13525 13623 13721 13819 13917 14015
## [145] 14113 14211 14309 14407 14505 14603 14701 14799 14897 14995 15093 15191
## [157] 15289 15387 15485 15583 15681 15779 15877 15975 16073 16171 16269 16367
## [169] 16465 16563 16661 16759 16857 16955 17053 17151 17249 17347 17445 17543
## [181] 17641 17739 17837 17935 18033 18131 18229 18327 18425 18523 18621 18719
## [193] 18817 18915 19013 19111 19209 19307 19405 19503 19601 19699 19797 19895
## [205] 19993 20091 20189 20287 20385 20483 20581 20679 20777 20875 20973 21071
## [217] 21169 21267 21365 21463 21561 21659 21757 21855 21953 22051 22149 22247
## [229] 22345 22443 22541 22639 22737 22835 22933 23031 23129 23227 23325 23423
## [241] 23521 23619 23717 23815 23913 24011 24109 24207 24305 24403 24501 24599
## [253] 24697 24795 24893 24991 25089 25187 25285 25383 25481 25579 25677 25775
## [265] 2 100 198 296 394 492 590 688 786 884 982 1080
## [277] 1178 1276 1374 1472 1570 1668 1766 1864 1962 2060 2158 2256
## [289] 2354 2452 2550 2648 2746 2844 2942 3040 3138 3236 3334 3432
## [301] 3530 3628 3726 3824 3922 4020 4118 4216 4314 4412 4510 4608
## [313] 4706 4804 4902 5000 5098 5196 5294 5392 5490 5588 5686 5784
## [325] 5882 5980 6078 6176 6274 6372 6470 6568 6666 6764 6862 6960
## [337] 7058 7156 7254 7352 7450 7548 7646 7744 7842 7940 8038 8136
## [349] 8234 8332 8430 8528 8626 8724 8822 8920 9018 9116 9214 9312
## [361] 9410 9508 9606 9704 9802 9900 9998 10096 10194 10292 10390 10488
## [373] 10586 10684 10782 10880 10978 11076 11174 11272 11370 11468 11566 11664
## [385] 11762 11860 11958 12056 12154 12252 12350 12448 12546 12644 12742 12840
## [397] 12938 13036 13134 13232 13330 13428 13526 13624 13722 13820 13918 14016
## [409] 14114 14212 14310 14408 14506 14604 14702 14800 14898 14996 15094 15192
## [421] 15290 15388 15486 15584 15682 15780 15878 15976 16074 16172 16270 16368
## [433] 16466 16564 16662 16760 16858 16956 17054 17152 17250 17348 17446 17544
## [445] 17642 17740 17838 17936 18034 18132 18230 18328 18426 18524 18622 18720
## [457] 18818 18916 19014 19112 19210 19308 19406 19504 19602 19700 19798 19896
## [469] 19994 20092 20190 20288 20386 20484 20582 20680 20778 20876 20974 21072
## [481] 21170 21268 21366 21464 21562 21660 21758 21856 21954 22052 22150 22248
## [493] 22346 22444 22542 22640 22738 22836 22934 23032 23130 23228 23326 23424
## [505] 23522 23620 23718 23816 23914 24012 24110 24208 24306 24404 24502 24600
## [517] 24698 24796 24894 24992 25090 25188 25286 25384 25482 25580 25678 25776
## [529] 3 101 199 297 395 493 591 689 787 885 983 1081
## [541] 1179 1277 1375 1473 1571 1669 1767 1865 1963 2061 2159 2257
## [553] 2355 2453 2551 2649 2747 2845 2943 3041 3139 3237 3335 3433
## [565] 3531 3629 3727 3825 3923 4021 4119 4217 4315 4413 4511 4609
## [577] 4707 4805 4903 5001 5099 5197 5295 5393 5491 5589 5687 5785
## [589] 5883 5981 6079 6177 6275 6373 6471 6569 6667 6765 6863 6961
## [601] 7059 7157 7255 7353 7451 7549 7647 7745 7843 7941 8039 8137
## [613] 8235 8333 8431 8529 8627 8725 8823 8921 9019 9117 9215 9313
## [625] 9411 9509 9607 9705 9803 9901 9999 10097 10195 10293 10391 10489
## [637] 10587 10685 10783 10881 10979 11077 11175 11273 11371 11469 11567 11665
## [649] 11763 11861 11959 12057 12155 12253 12351 12449 12547 12645 12743 12841
## [661] 12939 13037 13135 13233 13331 13429 13527 13625 13723 13821 13919 14017
## [673] 14115 14213 14311 14409 14507 14605 14703 14801 14899 14997 15095 15193
## [685] 15291 15389 15487 15585 15683 15781 15879 15977 16075 16173 16271 16369
## [697] 16467 16565 16663 16761 16859 16957 17055 17153 17251 17349 17447 17545
## [709] 17643 17741 17839 17937 18035 18133 18231 18329 18427 18525 18623 18721
## [721] 18819 18917 19015 19113 19211 19309 19407 19505 19603 19701 19799 19897
## [733] 19995 20093 20191 20289 20387 20485 20583 20681 20779 20877 20975 21073
## [745] 21171 21269 21367 21465 21563 21661 21759 21857 21955 22053 22151 22249
## [757] 22347 22445 22543 22641 22739 22837 22935 23033 23131 23229 23327 23425
## [769] 23523 23621 23719 23817 23915 24013 24111 24209 24307 24405 24503 24601
## [781] 24699 24797 24895 24993 25091 25189 25287 25385 25483 25581 25679 25777
## [793] 4 102 200 298 396 494 592 690 788 886 984 1082
## [805] 1180 1278 1376 1474 1572 1670 1768 1866 1964 2062 2160 2258
## [817] 2356 2454 2552 2650 2748 2846 2944 3042 3140 3238 3336 3434
## [829] 3532 3630 3728 3826 3924 4022 4120 4218 4316 4414 4512 4610
## [841] 4708 4806 4904 5002 5100 5198 5296 5394 5492 5590 5688 5786
## [853] 5884 5982 6080 6178 6276 6374 6472 6570 6668 6766 6864 6962
## [865] 7060 7158 7256 7354 7452 7550 7648 7746 7844 7942 8040 8138
## [877] 8236 8334 8432 8530 8628 8726 8824 8922 9020 9118 9216 9314
## [889] 9412 9510 9608 9706 9804 9902 10000 10098 10196 10294 10392 10490
## [901] 10588 10686 10784 10882 10980 11078 11176 11274 11372 11470 11568 11666
## [913] 11764 11862 11960 12058 12156 12254 12352 12450 12548 12646 12744 12842
## [925] 12940 13038 13136 13234 13332 13430 13528 13626 13724 13822 13920 14018
## [937] 14116 14214 14312 14410 14508 14606 14704 14802 14900 14998 15096 15194
## [949] 15292 15390 15488 15586 15684 15782 15880 15978 16076 16174 16272 16370
## [961] 16468 16566 16664 16762 16860 16958 17056 17154 17252 17350 17448 17546
## [973] 17644 17742 17840 17938 18036 18134 18232 18330 18428 18526 18624 18722
## [985] 18820 18918 19016 19114 19212 19310 19408 19506 19604 19702 19800 19898
## [997] 19996 20094 20192 20290 20388 20486 20584 20682 20780 20878 20976 21074
## [1009] 21172 21270 21368 21466 21564 21662 21760 21858 21956 22054 22152 22250
## [1021] 22348 22446 22544 22642 22740 22838 22936 23034 23132 23230 23328 23426
## [1033] 23524 23622 23720 23818 23916 24014 24112 24210 24308 24406 24504 24602
## [1045] 24700 24798 24896 24994 25092 25190 25288 25386 25484 25582 25680 25778
## [1057] 5 103 201 299 397 495 593 691 789 887 985 1083
## [1069] 1181 1279 1377 1475 1573 1671 1769 1867 1965 2063 2161 2259
## [1081] 2357 2455 2553 2651 2749 2847 2945 3043 3141 3239 3337 3435
## [1093] 3533 3631 3729 3827 3925 4023 4121 4219 4317 4415 4513 4611
## [1105] 4709 4807 4905 5003 5101 5199 5297 5395 5493 5591 5689 5787
## [1117] 5885 5983 6081 6179 6277 6375 6473 6571 6669 6767 6865 6963
## [1129] 7061 7159 7257 7355 7453 7551 7649 7747 7845 7943 8041 8139
## [1141] 8237 8335 8433 8531 8629 8727 8825 8923 9021 9119 9217 9315
## [1153] 9413 9511 9609 9707 9805 9903 10001 10099 10197 10295 10393 10491
## [1165] 10589 10687 10785 10883 10981 11079 11177 11275 11373 11471 11569 11667
## [1177] 11765 11863 11961 12059 12157 12255 12353 12451 12549 12647 12745 12843
## [1189] 12941 13039 13137 13235 13333 13431 13529 13627 13725 13823 13921 14019
## [1201] 14117 14215 14313 14411 14509 14607 14705 14803 14901 14999 15097 15195
## [1213] 15293 15391 15489 15587 15685 15783 15881 15979 16077 16175 16273 16371
## [1225] 16469 16567 16665 16763 16861 16959 17057 17155 17253 17351 17449 17547
## [1237] 17645 17743 17841 17939 18037 18135 18233 18331 18429 18527 18625 18723
## [1249] 18821 18919 19017 19115 19213 19311 19409 19507 19605 19703 19801 19899
## [1261] 19997 20095 20193 20291 20389 20487 20585 20683 20781 20879 20977 21075
## [1273] 21173 21271 21369 21467 21565 21663 21761 21859 21957 22055 22153 22251
## [1285] 22349 22447 22545 22643 22741 22839 22937 23035 23133 23231 23329 23427
## [1297] 23525 23623 23721 23819 23917 24015 24113 24211 24309 24407 24505 24603
## [1309] 24701 24799 24897 24995 25093 25191 25289 25387 25485 25583 25681 25779
## [1321] 6 104 202 300 398 496 594 692 790 888 986 1084
## [1333] 1182 1280 1378 1476 1574 1672 1770 1868 1966 2064 2162 2260
## [1345] 2358 2456 2554 2652 2750 2848 2946 3044 3142 3240 3338 3436
## [1357] 3534 3632 3730 3828 3926 4024 4122 4220 4318 4416 4514 4612
## [1369] 4710 4808 4906 5004 5102 5200 5298 5396 5494 5592 5690 5788
## [1381] 5886 5984 6082 6180 6278 6376 6474 6572 6670 6768 6866 6964
## [1393] 7062 7160 7258 7356 7454 7552 7650 7748 7846 7944 8042 8140
## [1405] 8238 8336 8434 8532 8630 8728 8826 8924 9022 9120 9218 9316
## [1417] 9414 9512 9610 9708 9806 9904 10002 10100 10198 10296 10394 10492
## [1429] 10590 10688 10786 10884 10982 11080 11178 11276 11374 11472 11570 11668
## [1441] 11766 11864 11962 12060 12158 12256 12354 12452 12550 12648 12746 12844
## [1453] 12942 13040 13138 13236 13334 13432 13530 13628 13726 13824 13922 14020
## [1465] 14118 14216 14314 14412 14510 14608 14706 14804 14902 15000 15098 15196
## [1477] 15294 15392 15490 15588 15686 15784 15882 15980 16078 16176 16274 16372
## [1489] 16470 16568 16666 16764 16862 16960 17058 17156 17254 17352 17450 17548
## [1501] 17646 17744 17842 17940 18038 18136 18234 18332 18430 18528 18626 18724
## [1513] 18822 18920 19018 19116 19214 19312 19410 19508 19606 19704 19802 19900
## [1525] 19998 20096 20194 20292 20390 20488 20586 20684 20782 20880 20978 21076
## [1537] 21174 21272 21370 21468 21566 21664 21762 21860 21958 22056 22154 22252
## [1549] 22350 22448 22546 22644 22742 22840 22938 23036 23134 23232 23330 23428
## [1561] 23526 23624 23722 23820 23918 24016 24114 24212 24310 24408 24506 24604
## [1573] 24702 24800 24898 24996 25094 25192 25290 25388 25486 25584 25682 25780
## [1585] 7 105 203 301 399 497 595 693 791 889 987 1085
## [1597] 1183 1281 1379 1477 1575 1673 1771 1869 1967 2065 2163 2261
## [1609] 2359 2457 2555 2653 2751 2849 2947 3045 3143 3241 3339 3437
## [1621] 3535 3633 3731 3829 3927 4025 4123 4221 4319 4417 4515 4613
## [1633] 4711 4809 4907 5005 5103 5201 5299 5397 5495 5593 5691 5789
## [1645] 5887 5985 6083 6181 6279 6377 6475 6573 6671 6769 6867 6965
## [1657] 7063 7161 7259 7357 7455 7553 7651 7749 7847 7945 8043 8141
## [1669] 8239 8337 8435 8533 8631 8729 8827 8925 9023 9121 9219 9317
## [1681] 9415 9513 9611 9709 9807 9905 10003 10101 10199 10297 10395 10493
## [1693] 10591 10689 10787 10885 10983 11081 11179 11277 11375 11473 11571 11669
## [1705] 11767 11865 11963 12061 12159 12257 12355 12453 12551 12649 12747 12845
## [1717] 12943 13041 13139 13237 13335 13433 13531 13629 13727 13825 13923 14021
## [1729] 14119 14217 14315 14413 14511 14609 14707 14805 14903 15001 15099 15197
## [1741] 15295 15393 15491 15589 15687 15785 15883 15981 16079 16177 16275 16373
## [1753] 16471 16569 16667 16765 16863 16961 17059 17157 17255 17353 17451 17549
## [1765] 17647 17745 17843 17941 18039 18137 18235 18333 18431 18529 18627 18725
## [1777] 18823 18921 19019 19117 19215 19313 19411 19509 19607 19705 19803 19901
## [1789] 19999 20097 20195 20293 20391 20489 20587 20685 20783 20881 20979 21077
## [1801] 21175 21273 21371 21469 21567 21665 21763 21861 21959 22057 22155 22253
## [1813] 22351 22449 22547 22645 22743 22841 22939 23037 23135 23233 23331 23429
## [1825] 23527 23625 23723 23821 23919 24017 24115 24213 24311 24409 24507 24605
## [1837] 24703 24801 24899 24997 25095 25193 25291 25389 25487 25585 25683 25781
## [1849] 8 106 204 302 400 498 596 694 792 890 988 1086
## [1861] 1184 1282 1380 1478 1576 1674 1772 1870 1968 2066 2164 2262
## [1873] 2360 2458 2556 2654 2752 2850 2948 3046 3144 3242 3340 3438
## [1885] 3536 3634 3732 3830 3928 4026 4124 4222 4320 4418 4516 4614
## [1897] 4712 4810 4908 5006 5104 5202 5300 5398 5496 5594 5692 5790
## [1909] 5888 5986 6084 6182 6280 6378 6476 6574 6672 6770 6868 6966
## [1921] 7064 7162 7260 7358 7456 7554 7652 7750 7848 7946 8044 8142
## [1933] 8240 8338 8436 8534 8632 8730 8828 8926 9024 9122 9220 9318
## [1945] 9416 9514 9612 9710 9808 9906 10004 10102 10200 10298 10396 10494
## [1957] 10592 10690 10788 10886 10984 11082 11180 11278 11376 11474 11572 11670
## [1969] 11768 11866 11964 12062 12160 12258 12356 12454 12552 12650 12748 12846
## [1981] 12944 13042 13140 13238 13336 13434 13532 13630 13728 13826 13924 14022
## [1993] 14120 14218 14316 14414 14512 14610 14708 14806 14904 15002 15100 15198
## [2005] 15296 15394 15492 15590 15688 15786 15884 15982 16080 16178 16276 16374
## [2017] 16472 16570 16668 16766 16864 16962 17060 17158 17256 17354 17452 17550
## [2029] 17648 17746 17844 17942 18040 18138 18236 18334 18432 18530 18628 18726
## [2041] 18824 18922 19020 19118 19216 19314 19412 19510 19608 19706 19804 19902
## [2053] 20000 20098 20196 20294 20392 20490 20588 20686 20784 20882 20980 21078
## [2065] 21176 21274 21372 21470 21568 21666 21764 21862 21960 22058 22156 22254
## [2077] 22352 22450 22548 22646 22744 22842 22940 23038 23136 23234 23332 23430
## [2089] 23528 23626 23724 23822 23920 24018 24116 24214 24312 24410 24508 24606
## [2101] 24704 24802 24900 24998 25096 25194 25292 25390 25488 25586 25684 25782
## [2113] 9 107 205 303 401 499 597 695 793 891 989 1087
## [2125] 1185 1283 1381 1479 1577 1675 1773 1871 1969 2067 2165 2263
## [2137] 2361 2459 2557 2655 2753 2851 2949 3047 3145 3243 3341 3439
## [2149] 3537 3635 3733 3831 3929 4027 4125 4223 4321 4419 4517 4615
## [2161] 4713 4811 4909 5007 5105 5203 5301 5399 5497 5595 5693 5791
## [2173] 5889 5987 6085 6183 6281 6379 6477 6575 6673 6771 6869 6967
## [2185] 7065 7163 7261 7359 7457 7555 7653 7751 7849 7947 8045 8143
## [2197] 8241 8339 8437 8535 8633 8731 8829 8927 9025 9123 9221 9319
## [2209] 9417 9515 9613 9711 9809 9907 10005 10103 10201 10299 10397 10495
## [2221] 10593 10691 10789 10887 10985 11083 11181 11279 11377 11475 11573 11671
## [2233] 11769 11867 11965 12063 12161 12259 12357 12455 12553 12651 12749 12847
## [2245] 12945 13043 13141 13239 13337 13435 13533 13631 13729 13827 13925 14023
## [2257] 14121 14219 14317 14415 14513 14611 14709 14807 14905 15003 15101 15199
## [2269] 15297 15395 15493 15591 15689 15787 15885 15983 16081 16179 16277 16375
## [2281] 16473 16571 16669 16767 16865 16963 17061 17159 17257 17355 17453 17551
## [2293] 17649 17747 17845 17943 18041 18139 18237 18335 18433 18531 18629 18727
## [2305] 18825 18923 19021 19119 19217 19315 19413 19511 19609 19707 19805 19903
## [2317] 20001 20099 20197 20295 20393 20491 20589 20687 20785 20883 20981 21079
## [2329] 21177 21275 21373 21471 21569 21667 21765 21863 21961 22059 22157 22255
## [2341] 22353 22451 22549 22647 22745 22843 22941 23039 23137 23235 23333 23431
## [2353] 23529 23627 23725 23823 23921 24019 24117 24215 24313 24411 24509 24607
## [2365] 24705 24803 24901 24999 25097 25195 25293 25391 25489 25587 25685 25783
## [2377] 10 108 206 304 402 500 598 696 794 892 990 1088
## [2389] 1186 1284 1382 1480 1578 1676 1774 1872 1970 2068 2166 2264
## [2401] 2362 2460 2558 2656 2754 2852 2950 3048 3146 3244 3342 3440
## [2413] 3538 3636 3734 3832 3930 4028 4126 4224 4322 4420 4518 4616
## [2425] 4714 4812 4910 5008 5106 5204 5302 5400 5498 5596 5694 5792
## [2437] 5890 5988 6086 6184 6282 6380 6478 6576 6674 6772 6870 6968
## [2449] 7066 7164 7262 7360 7458 7556 7654 7752 7850 7948 8046 8144
## [2461] 8242 8340 8438 8536 8634 8732 8830 8928 9026 9124 9222 9320
## [2473] 9418 9516 9614 9712 9810 9908 10006 10104 10202 10300 10398 10496
## [2485] 10594 10692 10790 10888 10986 11084 11182 11280 11378 11476 11574 11672
## [2497] 11770 11868 11966 12064 12162 12260 12358 12456 12554 12652 12750 12848
## [2509] 12946 13044 13142 13240 13338 13436 13534 13632 13730 13828 13926 14024
## [2521] 14122 14220 14318 14416 14514 14612 14710 14808 14906 15004 15102 15200
## [2533] 15298 15396 15494 15592 15690 15788 15886 15984 16082 16180 16278 16376
## [2545] 16474 16572 16670 16768 16866 16964 17062 17160 17258 17356 17454 17552
## [2557] 17650 17748 17846 17944 18042 18140 18238 18336 18434 18532 18630 18728
## [2569] 18826 18924 19022 19120 19218 19316 19414 19512 19610 19708 19806 19904
## [2581] 20002 20100 20198 20296 20394 20492 20590 20688 20786 20884 20982 21080
## [2593] 21178 21276 21374 21472 21570 21668 21766 21864 21962 22060 22158 22256
## [2605] 22354 22452 22550 22648 22746 22844 22942 23040 23138 23236 23334 23432
## [2617] 23530 23628 23726 23824 23922 24020 24118 24216 24314 24412 24510 24608
## [2629] 24706 24804 24902 25000 25098 25196 25294 25392 25490 25588 25686 25784
## [2641] 11 109 207 305 403 501 599 697 795 893 991 1089
## [2653] 1187 1285 1383 1481 1579 1677 1775 1873 1971 2069 2167 2265
## [2665] 2363 2461 2559 2657 2755 2853 2951 3049 3147 3245 3343 3441
## [2677] 3539 3637 3735 3833 3931 4029 4127 4225 4323 4421 4519 4617
## [2689] 4715 4813 4911 5009 5107 5205 5303 5401 5499 5597 5695 5793
## [2701] 5891 5989 6087 6185 6283 6381 6479 6577 6675 6773 6871 6969
## [2713] 7067 7165 7263 7361 7459 7557 7655 7753 7851 7949 8047 8145
## [2725] 8243 8341 8439 8537 8635 8733 8831 8929 9027 9125 9223 9321
## [2737] 9419 9517 9615 9713 9811 9909 10007 10105 10203 10301 10399 10497
## [2749] 10595 10693 10791 10889 10987 11085 11183 11281 11379 11477 11575 11673
## [2761] 11771 11869 11967 12065 12163 12261 12359 12457 12555 12653 12751 12849
## [2773] 12947 13045 13143 13241 13339 13437 13535 13633 13731 13829 13927 14025
## [2785] 14123 14221 14319 14417 14515 14613 14711 14809 14907 15005 15103 15201
## [2797] 15299 15397 15495 15593 15691 15789 15887 15985 16083 16181 16279 16377
## [2809] 16475 16573 16671 16769 16867 16965 17063 17161 17259 17357 17455 17553
## [2821] 17651 17749 17847 17945 18043 18141 18239 18337 18435 18533 18631 18729
## [2833] 18827 18925 19023 19121 19219 19317 19415 19513 19611 19709 19807 19905
## [2845] 20003 20101 20199 20297 20395 20493 20591 20689 20787 20885 20983 21081
## [2857] 21179 21277 21375 21473 21571 21669 21767 21865 21963 22061 22159 22257
## [2869] 22355 22453 22551 22649 22747 22845 22943 23041 23139 23237 23335 23433
## [2881] 23531 23629 23727 23825 23923 24021 24119 24217 24315 24413 24511 24609
## [2893] 24707 24805 24903 25001 25099 25197 25295 25393 25491 25589 25687 25785
## [2905] 12 110 208 306 404 502 600 698 796 894 992 1090
## [2917] 1188 1286 1384 1482 1580 1678 1776 1874 1972 2070 2168 2266
## [2929] 2364 2462 2560 2658 2756 2854 2952 3050 3148 3246 3344 3442
## [2941] 3540 3638 3736 3834 3932 4030 4128 4226 4324 4422 4520 4618
## [2953] 4716 4814 4912 5010 5108 5206 5304 5402 5500 5598 5696 5794
## [2965] 5892 5990 6088 6186 6284 6382 6480 6578 6676 6774 6872 6970
## [2977] 7068 7166 7264 7362 7460 7558 7656 7754 7852 7950 8048 8146
## [2989] 8244 8342 8440 8538 8636 8734 8832 8930 9028 9126 9224 9322
## [3001] 9420 9518 9616 9714 9812 9910 10008 10106 10204 10302 10400 10498
## [3013] 10596 10694 10792 10890 10988 11086 11184 11282 11380 11478 11576 11674
## [3025] 11772 11870 11968 12066 12164 12262 12360 12458 12556 12654 12752 12850
## [3037] 12948 13046 13144 13242 13340 13438 13536 13634 13732 13830 13928 14026
## [3049] 14124 14222 14320 14418 14516 14614 14712 14810 14908 15006 15104 15202
## [3061] 15300 15398 15496 15594 15692 15790 15888 15986 16084 16182 16280 16378
## [3073] 16476 16574 16672 16770 16868 16966 17064 17162 17260 17358 17456 17554
## [3085] 17652 17750 17848 17946 18044 18142 18240 18338 18436 18534 18632 18730
## [3097] 18828 18926 19024 19122 19220 19318 19416 19514 19612 19710 19808 19906
## [3109] 20004 20102 20200 20298 20396 20494 20592 20690 20788 20886 20984 21082
## [3121] 21180 21278 21376 21474 21572 21670 21768 21866 21964 22062 22160 22258
## [3133] 22356 22454 22552 22650 22748 22846 22944 23042 23140 23238 23336 23434
## [3145] 23532 23630 23728 23826 23924 24022 24120 24218 24316 24414 24512 24610
## [3157] 24708 24806 24904 25002 25100 25198 25296 25394 25492 25590 25688 25786
## [3169] 13 111 209 307 405 503 601 699 797 895 993 1091
## [3181] 1189 1287 1385 1483 1581 1679 1777 1875 1973 2071 2169 2267
## [3193] 2365 2463 2561 2659 2757 2855 2953 3051 3149 3247 3345 3443
## [3205] 3541 3639 3737 3835 3933 4031 4129 4227 4325 4423 4521 4619
## [3217] 4717 4815 4913 5011 5109 5207 5305 5403 5501 5599 5697 5795
## [3229] 5893 5991 6089 6187 6285 6383 6481 6579 6677 6775 6873 6971
## [3241] 7069 7167 7265 7363 7461 7559 7657 7755 7853 7951 8049 8147
## [3253] 8245 8343 8441 8539 8637 8735 8833 8931 9029 9127 9225 9323
## [3265] 9421 9519 9617 9715 9813 9911 10009 10107 10205 10303 10401 10499
## [3277] 10597 10695 10793 10891 10989 11087 11185 11283 11381 11479 11577 11675
## [3289] 11773 11871 11969 12067 12165 12263 12361 12459 12557 12655 12753 12851
## [3301] 12949 13047 13145 13243 13341 13439 13537 13635 13733 13831 13929 14027
## [3313] 14125 14223 14321 14419 14517 14615 14713 14811 14909 15007 15105 15203
## [3325] 15301 15399 15497 15595 15693 15791 15889 15987 16085 16183 16281 16379
## [3337] 16477 16575 16673 16771 16869 16967 17065 17163 17261 17359 17457 17555
## [3349] 17653 17751 17849 17947 18045 18143 18241 18339 18437 18535 18633 18731
## [3361] 18829 18927 19025 19123 19221 19319 19417 19515 19613 19711 19809 19907
## [3373] 20005 20103 20201 20299 20397 20495 20593 20691 20789 20887 20985 21083
## [3385] 21181 21279 21377 21475 21573 21671 21769 21867 21965 22063 22161 22259
## [3397] 22357 22455 22553 22651 22749 22847 22945 23043 23141 23239 23337 23435
## [3409] 23533 23631 23729 23827 23925 24023 24121 24219 24317 24415 24513 24611
## [3421] 24709 24807 24905 25003 25101 25199 25297 25395 25493 25591 25689 25787
## [3433] 14 112 210 308 406 504 602 700 798 896 994 1092
## [3445] 1190 1288 1386 1484 1582 1680 1778 1876 1974 2072 2170 2268
## [3457] 2366 2464 2562 2660 2758 2856 2954 3052 3150 3248 3346 3444
## [3469] 3542 3640 3738 3836 3934 4032 4130 4228 4326 4424 4522 4620
## [3481] 4718 4816 4914 5012 5110 5208 5306 5404 5502 5600 5698 5796
## [3493] 5894 5992 6090 6188 6286 6384 6482 6580 6678 6776 6874 6972
## [3505] 7070 7168 7266 7364 7462 7560 7658 7756 7854 7952 8050 8148
## [3517] 8246 8344 8442 8540 8638 8736 8834 8932 9030 9128 9226 9324
## [3529] 9422 9520 9618 9716 9814 9912 10010 10108 10206 10304 10402 10500
## [3541] 10598 10696 10794 10892 10990 11088 11186 11284 11382 11480 11578 11676
## [3553] 11774 11872 11970 12068 12166 12264 12362 12460 12558 12656 12754 12852
## [3565] 12950 13048 13146 13244 13342 13440 13538 13636 13734 13832 13930 14028
## [3577] 14126 14224 14322 14420 14518 14616 14714 14812 14910 15008 15106 15204
## [3589] 15302 15400 15498 15596 15694 15792 15890 15988 16086 16184 16282 16380
## [3601] 16478 16576 16674 16772 16870 16968 17066 17164 17262 17360 17458 17556
## [3613] 17654 17752 17850 17948 18046 18144 18242 18340 18438 18536 18634 18732
## [3625] 18830 18928 19026 19124 19222 19320 19418 19516 19614 19712 19810 19908
## [3637] 20006 20104 20202 20300 20398 20496 20594 20692 20790 20888 20986 21084
## [3649] 21182 21280 21378 21476 21574 21672 21770 21868 21966 22064 22162 22260
## [3661] 22358 22456 22554 22652 22750 22848 22946 23044 23142 23240 23338 23436
## [3673] 23534 23632 23730 23828 23926 24024 24122 24220 24318 24416 24514 24612
## [3685] 24710 24808 24906 25004 25102 25200 25298 25396 25494 25592 25690 25788
## [3697] 15 113 211 309 407 505 603 701 799 897 995 1093
## [3709] 1191 1289 1387 1485 1583 1681 1779 1877 1975 2073 2171 2269
## [3721] 2367 2465 2563 2661 2759 2857 2955 3053 3151 3249 3347 3445
## [3733] 3543 3641 3739 3837 3935 4033 4131 4229 4327 4425 4523 4621
## [3745] 4719 4817 4915 5013 5111 5209 5307 5405 5503 5601 5699 5797
## [3757] 5895 5993 6091 6189 6287 6385 6483 6581 6679 6777 6875 6973
## [3769] 7071 7169 7267 7365 7463 7561 7659 7757 7855 7953 8051 8149
## [3781] 8247 8345 8443 8541 8639 8737 8835 8933 9031 9129 9227 9325
## [3793] 9423 9521 9619 9717 9815 9913 10011 10109 10207 10305 10403 10501
## [3805] 10599 10697 10795 10893 10991 11089 11187 11285 11383 11481 11579 11677
## [3817] 11775 11873 11971 12069 12167 12265 12363 12461 12559 12657 12755 12853
## [3829] 12951 13049 13147 13245 13343 13441 13539 13637 13735 13833 13931 14029
## [3841] 14127 14225 14323 14421 14519 14617 14715 14813 14911 15009 15107 15205
## [3853] 15303 15401 15499 15597 15695 15793 15891 15989 16087 16185 16283 16381
## [3865] 16479 16577 16675 16773 16871 16969 17067 17165 17263 17361 17459 17557
## [3877] 17655 17753 17851 17949 18047 18145 18243 18341 18439 18537 18635 18733
## [3889] 18831 18929 19027 19125 19223 19321 19419 19517 19615 19713 19811 19909
## [3901] 20007 20105 20203 20301 20399 20497 20595 20693 20791 20889 20987 21085
## [3913] 21183 21281 21379 21477 21575 21673 21771 21869 21967 22065 22163 22261
## [3925] 22359 22457 22555 22653 22751 22849 22947 23045 23143 23241 23339 23437
## [3937] 23535 23633 23731 23829 23927 24025 24123 24221 24319 24417 24515 24613
## [3949] 24711 24809 24907 25005 25103 25201 25299 25397 25495 25593 25691 25789
## [3961] 16 114 212 310 408 506 604 702 800 898 996 1094
## [3973] 1192 1290 1388 1486 1584 1682 1780 1878 1976 2074 2172 2270
## [3985] 2368 2466 2564 2662 2760 2858 2956 3054 3152 3250 3348 3446
## [3997] 3544 3642 3740 3838 3936 4034 4132 4230 4328 4426 4524 4622
## [4009] 4720 4818 4916 5014 5112 5210 5308 5406 5504 5602 5700 5798
## [4021] 5896 5994 6092 6190 6288 6386 6484 6582 6680 6778 6876 6974
## [4033] 7072 7170 7268 7366 7464 7562 7660 7758 7856 7954 8052 8150
## [4045] 8248 8346 8444 8542 8640 8738 8836 8934 9032 9130 9228 9326
## [4057] 9424 9522 9620 9718 9816 9914 10012 10110 10208 10306 10404 10502
## [4069] 10600 10698 10796 10894 10992 11090 11188 11286 11384 11482 11580 11678
## [4081] 11776 11874 11972 12070 12168 12266 12364 12462 12560 12658 12756 12854
## [4093] 12952 13050 13148 13246 13344 13442 13540 13638 13736 13834 13932 14030
## [4105] 14128 14226 14324 14422 14520 14618 14716 14814 14912 15010 15108 15206
## [4117] 15304 15402 15500 15598 15696 15794 15892 15990 16088 16186 16284 16382
## [4129] 16480 16578 16676 16774 16872 16970 17068 17166 17264 17362 17460 17558
## [4141] 17656 17754 17852 17950 18048 18146 18244 18342 18440 18538 18636 18734
## [4153] 18832 18930 19028 19126 19224 19322 19420 19518 19616 19714 19812 19910
## [4165] 20008 20106 20204 20302 20400 20498 20596 20694 20792 20890 20988 21086
## [4177] 21184 21282 21380 21478 21576 21674 21772 21870 21968 22066 22164 22262
## [4189] 22360 22458 22556 22654 22752 22850 22948 23046 23144 23242 23340 23438
## [4201] 23536 23634 23732 23830 23928 24026 24124 24222 24320 24418 24516 24614
## [4213] 24712 24810 24908 25006 25104 25202 25300 25398 25496 25594 25692 25790
## [4225] 17 115 213 311 409 507 605 703 801 899 997 1095
## [4237] 1193 1291 1389 1487 1585 1683 1781 1879 1977 2075 2173 2271
## [4249] 2369 2467 2565 2663 2761 2859 2957 3055 3153 3251 3349 3447
## [4261] 3545 3643 3741 3839 3937 4035 4133 4231 4329 4427 4525 4623
## [4273] 4721 4819 4917 5015 5113 5211 5309 5407 5505 5603 5701 5799
## [4285] 5897 5995 6093 6191 6289 6387 6485 6583 6681 6779 6877 6975
## [4297] 7073 7171 7269 7367 7465 7563 7661 7759 7857 7955 8053 8151
## [4309] 8249 8347 8445 8543 8641 8739 8837 8935 9033 9131 9229 9327
## [4321] 9425 9523 9621 9719 9817 9915 10013 10111 10209 10307 10405 10503
## [4333] 10601 10699 10797 10895 10993 11091 11189 11287 11385 11483 11581 11679
## [4345] 11777 11875 11973 12071 12169 12267 12365 12463 12561 12659 12757 12855
## [4357] 12953 13051 13149 13247 13345 13443 13541 13639 13737 13835 13933 14031
## [4369] 14129 14227 14325 14423 14521 14619 14717 14815 14913 15011 15109 15207
## [4381] 15305 15403 15501 15599 15697 15795 15893 15991 16089 16187 16285 16383
## [4393] 16481 16579 16677 16775 16873 16971 17069 17167 17265 17363 17461 17559
## [4405] 17657 17755 17853 17951 18049 18147 18245 18343 18441 18539 18637 18735
## [4417] 18833 18931 19029 19127 19225 19323 19421 19519 19617 19715 19813 19911
## [4429] 20009 20107 20205 20303 20401 20499 20597 20695 20793 20891 20989 21087
## [4441] 21185 21283 21381 21479 21577 21675 21773 21871 21969 22067 22165 22263
## [4453] 22361 22459 22557 22655 22753 22851 22949 23047 23145 23243 23341 23439
## [4465] 23537 23635 23733 23831 23929 24027 24125 24223 24321 24419 24517 24615
## [4477] 24713 24811 24909 25007 25105 25203 25301 25399 25497 25595 25693 25791
## [4489] 18 116 214 312 410 508 606 704 802 900 998 1096
## [4501] 1194 1292 1390 1488 1586 1684 1782 1880 1978 2076 2174 2272
## [4513] 2370 2468 2566 2664 2762 2860 2958 3056 3154 3252 3350 3448
## [4525] 3546 3644 3742 3840 3938 4036 4134 4232 4330 4428 4526 4624
## [4537] 4722 4820 4918 5016 5114 5212 5310 5408 5506 5604 5702 5800
## [4549] 5898 5996 6094 6192 6290 6388 6486 6584 6682 6780 6878 6976
## [4561] 7074 7172 7270 7368 7466 7564 7662 7760 7858 7956 8054 8152
## [4573] 8250 8348 8446 8544 8642 8740 8838 8936 9034 9132 9230 9328
## [4585] 9426 9524 9622 9720 9818 9916 10014 10112 10210 10308 10406 10504
## [4597] 10602 10700 10798 10896 10994 11092 11190 11288 11386 11484 11582 11680
## [4609] 11778 11876 11974 12072 12170 12268 12366 12464 12562 12660 12758 12856
## [4621] 12954 13052 13150 13248 13346 13444 13542 13640 13738 13836 13934 14032
## [4633] 14130 14228 14326 14424 14522 14620 14718 14816 14914 15012 15110 15208
## [4645] 15306 15404 15502 15600 15698 15796 15894 15992 16090 16188 16286 16384
## [4657] 16482 16580 16678 16776 16874 16972 17070 17168 17266 17364 17462 17560
## [4669] 17658 17756 17854 17952 18050 18148 18246 18344 18442 18540 18638 18736
## [4681] 18834 18932 19030 19128 19226 19324 19422 19520 19618 19716 19814 19912
## [4693] 20010 20108 20206 20304 20402 20500 20598 20696 20794 20892 20990 21088
## [4705] 21186 21284 21382 21480 21578 21676 21774 21872 21970 22068 22166 22264
## [4717] 22362 22460 22558 22656 22754 22852 22950 23048 23146 23244 23342 23440
## [4729] 23538 23636 23734 23832 23930 24028 24126 24224 24322 24420 24518 24616
## [4741] 24714 24812 24910 25008 25106 25204 25302 25400 25498 25596 25694 25792
## [4753] 19 117 215 313 411 509 607 705 803 901 999 1097
## [4765] 1195 1293 1391 1489 1587 1685 1783 1881 1979 2077 2175 2273
## [4777] 2371 2469 2567 2665 2763 2861 2959 3057 3155 3253 3351 3449
## [4789] 3547 3645 3743 3841 3939 4037 4135 4233 4331 4429 4527 4625
## [4801] 4723 4821 4919 5017 5115 5213 5311 5409 5507 5605 5703 5801
## [4813] 5899 5997 6095 6193 6291 6389 6487 6585 6683 6781 6879 6977
## [4825] 7075 7173 7271 7369 7467 7565 7663 7761 7859 7957 8055 8153
## [4837] 8251 8349 8447 8545 8643 8741 8839 8937 9035 9133 9231 9329
## [4849] 9427 9525 9623 9721 9819 9917 10015 10113 10211 10309 10407 10505
## [4861] 10603 10701 10799 10897 10995 11093 11191 11289 11387 11485 11583 11681
## [4873] 11779 11877 11975 12073 12171 12269 12367 12465 12563 12661 12759 12857
## [4885] 12955 13053 13151 13249 13347 13445 13543 13641 13739 13837 13935 14033
## [4897] 14131 14229 14327 14425 14523 14621 14719 14817 14915 15013 15111 15209
## [4909] 15307 15405 15503 15601 15699 15797 15895 15993 16091 16189 16287 16385
## [4921] 16483 16581 16679 16777 16875 16973 17071 17169 17267 17365 17463 17561
## [4933] 17659 17757 17855 17953 18051 18149 18247 18345 18443 18541 18639 18737
## [4945] 18835 18933 19031 19129 19227 19325 19423 19521 19619 19717 19815 19913
## [4957] 20011 20109 20207 20305 20403 20501 20599 20697 20795 20893 20991 21089
## [4969] 21187 21285 21383 21481 21579 21677 21775 21873 21971 22069 22167 22265
## [4981] 22363 22461 22559 22657 22755 22853 22951 23049 23147 23245 23343 23441
## [4993] 23539 23637 23735 23833 23931 24029 24127 24225 24323 24421 24519 24617
## [5005] 24715 24813 24911 25009 25107 25205 25303 25401 25499 25597 25695 25793
## [5017] 20 118 216 314 412 510 608 706 804 902 1000 1098
## [5029] 1196 1294 1392 1490 1588 1686 1784 1882 1980 2078 2176 2274
## [5041] 2372 2470 2568 2666 2764 2862 2960 3058 3156 3254 3352 3450
## [5053] 3548 3646 3744 3842 3940 4038 4136 4234 4332 4430 4528 4626
## [5065] 4724 4822 4920 5018 5116 5214 5312 5410 5508 5606 5704 5802
## [5077] 5900 5998 6096 6194 6292 6390 6488 6586 6684 6782 6880 6978
## [5089] 7076 7174 7272 7370 7468 7566 7664 7762 7860 7958 8056 8154
## [5101] 8252 8350 8448 8546 8644 8742 8840 8938 9036 9134 9232 9330
## [5113] 9428 9526 9624 9722 9820 9918 10016 10114 10212 10310 10408 10506
## [5125] 10604 10702 10800 10898 10996 11094 11192 11290 11388 11486 11584 11682
## [5137] 11780 11878 11976 12074 12172 12270 12368 12466 12564 12662 12760 12858
## [5149] 12956 13054 13152 13250 13348 13446 13544 13642 13740 13838 13936 14034
## [5161] 14132 14230 14328 14426 14524 14622 14720 14818 14916 15014 15112 15210
## [5173] 15308 15406 15504 15602 15700 15798 15896 15994 16092 16190 16288 16386
## [5185] 16484 16582 16680 16778 16876 16974 17072 17170 17268 17366 17464 17562
## [5197] 17660 17758 17856 17954 18052 18150 18248 18346 18444 18542 18640 18738
## [5209] 18836 18934 19032 19130 19228 19326 19424 19522 19620 19718 19816 19914
## [5221] 20012 20110 20208 20306 20404 20502 20600 20698 20796 20894 20992 21090
## [5233] 21188 21286 21384 21482 21580 21678 21776 21874 21972 22070 22168 22266
## [5245] 22364 22462 22560 22658 22756 22854 22952 23050 23148 23246 23344 23442
## [5257] 23540 23638 23736 23834 23932 24030 24128 24226 24324 24422 24520 24618
## [5269] 24716 24814 24912 25010 25108 25206 25304 25402 25500 25598 25696 25794
## [5281] 21 119 217 315 413 511 609 707 805 903 1001 1099
## [5293] 1197 1295 1393 1491 1589 1687 1785 1883 1981 2079 2177 2275
## [5305] 2373 2471 2569 2667 2765 2863 2961 3059 3157 3255 3353 3451
## [5317] 3549 3647 3745 3843 3941 4039 4137 4235 4333 4431 4529 4627
## [5329] 4725 4823 4921 5019 5117 5215 5313 5411 5509 5607 5705 5803
## [5341] 5901 5999 6097 6195 6293 6391 6489 6587 6685 6783 6881 6979
## [5353] 7077 7175 7273 7371 7469 7567 7665 7763 7861 7959 8057 8155
## [5365] 8253 8351 8449 8547 8645 8743 8841 8939 9037 9135 9233 9331
## [5377] 9429 9527 9625 9723 9821 9919 10017 10115 10213 10311 10409 10507
## [5389] 10605 10703 10801 10899 10997 11095 11193 11291 11389 11487 11585 11683
## [5401] 11781 11879 11977 12075 12173 12271 12369 12467 12565 12663 12761 12859
## [5413] 12957 13055 13153 13251 13349 13447 13545 13643 13741 13839 13937 14035
## [5425] 14133 14231 14329 14427 14525 14623 14721 14819 14917 15015 15113 15211
## [5437] 15309 15407 15505 15603 15701 15799 15897 15995 16093 16191 16289 16387
## [5449] 16485 16583 16681 16779 16877 16975 17073 17171 17269 17367 17465 17563
## [5461] 17661 17759 17857 17955 18053 18151 18249 18347 18445 18543 18641 18739
## [5473] 18837 18935 19033 19131 19229 19327 19425 19523 19621 19719 19817 19915
## [5485] 20013 20111 20209 20307 20405 20503 20601 20699 20797 20895 20993 21091
## [5497] 21189 21287 21385 21483 21581 21679 21777 21875 21973 22071 22169 22267
## [5509] 22365 22463 22561 22659 22757 22855 22953 23051 23149 23247 23345 23443
## [5521] 23541 23639 23737 23835 23933 24031 24129 24227 24325 24423 24521 24619
## [5533] 24717 24815 24913 25011 25109 25207 25305 25403 25501 25599 25697 25795
## [5545] 22 120 218 316 414 512 610 708 806 904 1002 1100
## [5557] 1198 1296 1394 1492 1590 1688 1786 1884 1982 2080 2178 2276
## [5569] 2374 2472 2570 2668 2766 2864 2962 3060 3158 3256 3354 3452
## [5581] 3550 3648 3746 3844 3942 4040 4138 4236 4334 4432 4530 4628
## [5593] 4726 4824 4922 5020 5118 5216 5314 5412 5510 5608 5706 5804
## [5605] 5902 6000 6098 6196 6294 6392 6490 6588 6686 6784 6882 6980
## [5617] 7078 7176 7274 7372 7470 7568 7666 7764 7862 7960 8058 8156
## [5629] 8254 8352 8450 8548 8646 8744 8842 8940 9038 9136 9234 9332
## [5641] 9430 9528 9626 9724 9822 9920 10018 10116 10214 10312 10410 10508
## [5653] 10606 10704 10802 10900 10998 11096 11194 11292 11390 11488 11586 11684
## [5665] 11782 11880 11978 12076 12174 12272 12370 12468 12566 12664 12762 12860
## [5677] 12958 13056 13154 13252 13350 13448 13546 13644 13742 13840 13938 14036
## [5689] 14134 14232 14330 14428 14526 14624 14722 14820 14918 15016 15114 15212
## [5701] 15310 15408 15506 15604 15702 15800 15898 15996 16094 16192 16290 16388
## [5713] 16486 16584 16682 16780 16878 16976 17074 17172 17270 17368 17466 17564
## [5725] 17662 17760 17858 17956 18054 18152 18250 18348 18446 18544 18642 18740
## [5737] 18838 18936 19034 19132 19230 19328 19426 19524 19622 19720 19818 19916
## [5749] 20014 20112 20210 20308 20406 20504 20602 20700 20798 20896 20994 21092
## [5761] 21190 21288 21386 21484 21582 21680 21778 21876 21974 22072 22170 22268
## [5773] 22366 22464 22562 22660 22758 22856 22954 23052 23150 23248 23346 23444
## [5785] 23542 23640 23738 23836 23934 24032 24130 24228 24326 24424 24522 24620
## [5797] 24718 24816 24914 25012 25110 25208 25306 25404 25502 25600 25698 25796
## [5809] 23 121 219 317 415 513 611 709 807 905 1003 1101
## [5821] 1199 1297 1395 1493 1591 1689 1787 1885 1983 2081 2179 2277
## [5833] 2375 2473 2571 2669 2767 2865 2963 3061 3159 3257 3355 3453
## [5845] 3551 3649 3747 3845 3943 4041 4139 4237 4335 4433 4531 4629
## [5857] 4727 4825 4923 5021 5119 5217 5315 5413 5511 5609 5707 5805
## [5869] 5903 6001 6099 6197 6295 6393 6491 6589 6687 6785 6883 6981
## [5881] 7079 7177 7275 7373 7471 7569 7667 7765 7863 7961 8059 8157
## [5893] 8255 8353 8451 8549 8647 8745 8843 8941 9039 9137 9235 9333
## [5905] 9431 9529 9627 9725 9823 9921 10019 10117 10215 10313 10411 10509
## [5917] 10607 10705 10803 10901 10999 11097 11195 11293 11391 11489 11587 11685
## [5929] 11783 11881 11979 12077 12175 12273 12371 12469 12567 12665 12763 12861
## [5941] 12959 13057 13155 13253 13351 13449 13547 13645 13743 13841 13939 14037
## [5953] 14135 14233 14331 14429 14527 14625 14723 14821 14919 15017 15115 15213
## [5965] 15311 15409 15507 15605 15703 15801 15899 15997 16095 16193 16291 16389
## [5977] 16487 16585 16683 16781 16879 16977 17075 17173 17271 17369 17467 17565
## [5989] 17663 17761 17859 17957 18055 18153 18251 18349 18447 18545 18643 18741
## [6001] 18839 18937 19035 19133 19231 19329 19427 19525 19623 19721 19819 19917
## [6013] 20015 20113 20211 20309 20407 20505 20603 20701 20799 20897 20995 21093
## [6025] 21191 21289 21387 21485 21583 21681 21779 21877 21975 22073 22171 22269
## [6037] 22367 22465 22563 22661 22759 22857 22955 23053 23151 23249 23347 23445
## [6049] 23543 23641 23739 23837 23935 24033 24131 24229 24327 24425 24523 24621
## [6061] 24719 24817 24915 25013 25111 25209 25307 25405 25503 25601 25699 25797
## [6073] 24 122 220 318 416 514 612 710 808 906 1004 1102
## [6085] 1200 1298 1396 1494 1592 1690 1788 1886 1984 2082 2180 2278
## [6097] 2376 2474 2572 2670 2768 2866 2964 3062 3160 3258 3356 3454
## [6109] 3552 3650 3748 3846 3944 4042 4140 4238 4336 4434 4532 4630
## [6121] 4728 4826 4924 5022 5120 5218 5316 5414 5512 5610 5708 5806
## [6133] 5904 6002 6100 6198 6296 6394 6492 6590 6688 6786 6884 6982
## [6145] 7080 7178 7276 7374 7472 7570 7668 7766 7864 7962 8060 8158
## [6157] 8256 8354 8452 8550 8648 8746 8844 8942 9040 9138 9236 9334
## [6169] 9432 9530 9628 9726 9824 9922 10020 10118 10216 10314 10412 10510
## [6181] 10608 10706 10804 10902 11000 11098 11196 11294 11392 11490 11588 11686
## [6193] 11784 11882 11980 12078 12176 12274 12372 12470 12568 12666 12764 12862
## [6205] 12960 13058 13156 13254 13352 13450 13548 13646 13744 13842 13940 14038
## [6217] 14136 14234 14332 14430 14528 14626 14724 14822 14920 15018 15116 15214
## [6229] 15312 15410 15508 15606 15704 15802 15900 15998 16096 16194 16292 16390
## [6241] 16488 16586 16684 16782 16880 16978 17076 17174 17272 17370 17468 17566
## [6253] 17664 17762 17860 17958 18056 18154 18252 18350 18448 18546 18644 18742
## [6265] 18840 18938 19036 19134 19232 19330 19428 19526 19624 19722 19820 19918
## [6277] 20016 20114 20212 20310 20408 20506 20604 20702 20800 20898 20996 21094
## [6289] 21192 21290 21388 21486 21584 21682 21780 21878 21976 22074 22172 22270
## [6301] 22368 22466 22564 22662 22760 22858 22956 23054 23152 23250 23348 23446
## [6313] 23544 23642 23740 23838 23936 24034 24132 24230 24328 24426 24524 24622
## [6325] 24720 24818 24916 25014 25112 25210 25308 25406 25504 25602 25700 25798
## [6337] 25 123 221 319 417 515 613 711 809 907 1005 1103
## [6349] 1201 1299 1397 1495 1593 1691 1789 1887 1985 2083 2181 2279
## [6361] 2377 2475 2573 2671 2769 2867 2965 3063 3161 3259 3357 3455
## [6373] 3553 3651 3749 3847 3945 4043 4141 4239 4337 4435 4533 4631
## [6385] 4729 4827 4925 5023 5121 5219 5317 5415 5513 5611 5709 5807
## [6397] 5905 6003 6101 6199 6297 6395 6493 6591 6689 6787 6885 6983
## [6409] 7081 7179 7277 7375 7473 7571 7669 7767 7865 7963 8061 8159
## [6421] 8257 8355 8453 8551 8649 8747 8845 8943 9041 9139 9237 9335
## [6433] 9433 9531 9629 9727 9825 9923 10021 10119 10217 10315 10413 10511
## [6445] 10609 10707 10805 10903 11001 11099 11197 11295 11393 11491 11589 11687
## [6457] 11785 11883 11981 12079 12177 12275 12373 12471 12569 12667 12765 12863
## [6469] 12961 13059 13157 13255 13353 13451 13549 13647 13745 13843 13941 14039
## [6481] 14137 14235 14333 14431 14529 14627 14725 14823 14921 15019 15117 15215
## [6493] 15313 15411 15509 15607 15705 15803 15901 15999 16097 16195 16293 16391
## [6505] 16489 16587 16685 16783 16881 16979 17077 17175 17273 17371 17469 17567
## [6517] 17665 17763 17861 17959 18057 18155 18253 18351 18449 18547 18645 18743
## [6529] 18841 18939 19037 19135 19233 19331 19429 19527 19625 19723 19821 19919
## [6541] 20017 20115 20213 20311 20409 20507 20605 20703 20801 20899 20997 21095
## [6553] 21193 21291 21389 21487 21585 21683 21781 21879 21977 22075 22173 22271
## [6565] 22369 22467 22565 22663 22761 22859 22957 23055 23153 23251 23349 23447
## [6577] 23545 23643 23741 23839 23937 24035 24133 24231 24329 24427 24525 24623
## [6589] 24721 24819 24917 25015 25113 25211 25309 25407 25505 25603 25701 25799
## [6601] 26 124 222 320 418 516 614 712 810 908 1006 1104
## [6613] 1202 1300 1398 1496 1594 1692 1790 1888 1986 2084 2182 2280
## [6625] 2378 2476 2574 2672 2770 2868 2966 3064 3162 3260 3358 3456
## [6637] 3554 3652 3750 3848 3946 4044 4142 4240 4338 4436 4534 4632
## [6649] 4730 4828 4926 5024 5122 5220 5318 5416 5514 5612 5710 5808
## [6661] 5906 6004 6102 6200 6298 6396 6494 6592 6690 6788 6886 6984
## [6673] 7082 7180 7278 7376 7474 7572 7670 7768 7866 7964 8062 8160
## [6685] 8258 8356 8454 8552 8650 8748 8846 8944 9042 9140 9238 9336
## [6697] 9434 9532 9630 9728 9826 9924 10022 10120 10218 10316 10414 10512
## [6709] 10610 10708 10806 10904 11002 11100 11198 11296 11394 11492 11590 11688
## [6721] 11786 11884 11982 12080 12178 12276 12374 12472 12570 12668 12766 12864
## [6733] 12962 13060 13158 13256 13354 13452 13550 13648 13746 13844 13942 14040
## [6745] 14138 14236 14334 14432 14530 14628 14726 14824 14922 15020 15118 15216
## [6757] 15314 15412 15510 15608 15706 15804 15902 16000 16098 16196 16294 16392
## [6769] 16490 16588 16686 16784 16882 16980 17078 17176 17274 17372 17470 17568
## [6781] 17666 17764 17862 17960 18058 18156 18254 18352 18450 18548 18646 18744
## [6793] 18842 18940 19038 19136 19234 19332 19430 19528 19626 19724 19822 19920
## [6805] 20018 20116 20214 20312 20410 20508 20606 20704 20802 20900 20998 21096
## [6817] 21194 21292 21390 21488 21586 21684 21782 21880 21978 22076 22174 22272
## [6829] 22370 22468 22566 22664 22762 22860 22958 23056 23154 23252 23350 23448
## [6841] 23546 23644 23742 23840 23938 24036 24134 24232 24330 24428 24526 24624
## [6853] 24722 24820 24918 25016 25114 25212 25310 25408 25506 25604 25702 25800
## [6865] 27 125 223 321 419 517 615 713 811 909 1007 1105
## [6877] 1203 1301 1399 1497 1595 1693 1791 1889 1987 2085 2183 2281
## [6889] 2379 2477 2575 2673 2771 2869 2967 3065 3163 3261 3359 3457
## [6901] 3555 3653 3751 3849 3947 4045 4143 4241 4339 4437 4535 4633
## [6913] 4731 4829 4927 5025 5123 5221 5319 5417 5515 5613 5711 5809
## [6925] 5907 6005 6103 6201 6299 6397 6495 6593 6691 6789 6887 6985
## [6937] 7083 7181 7279 7377 7475 7573 7671 7769 7867 7965 8063 8161
## [6949] 8259 8357 8455 8553 8651 8749 8847 8945 9043 9141 9239 9337
## [6961] 9435 9533 9631 9729 9827 9925 10023 10121 10219 10317 10415 10513
## [6973] 10611 10709 10807 10905 11003 11101 11199 11297 11395 11493 11591 11689
## [6985] 11787 11885 11983 12081 12179 12277 12375 12473 12571 12669 12767 12865
## [6997] 12963 13061 13159 13257 13355 13453 13551 13649 13747 13845 13943 14041
## [7009] 14139 14237 14335 14433 14531 14629 14727 14825 14923 15021 15119 15217
## [7021] 15315 15413 15511 15609 15707 15805 15903 16001 16099 16197 16295 16393
## [7033] 16491 16589 16687 16785 16883 16981 17079 17177 17275 17373 17471 17569
## [7045] 17667 17765 17863 17961 18059 18157 18255 18353 18451 18549 18647 18745
## [7057] 18843 18941 19039 19137 19235 19333 19431 19529 19627 19725 19823 19921
## [7069] 20019 20117 20215 20313 20411 20509 20607 20705 20803 20901 20999 21097
## [7081] 21195 21293 21391 21489 21587 21685 21783 21881 21979 22077 22175 22273
## [7093] 22371 22469 22567 22665 22763 22861 22959 23057 23155 23253 23351 23449
## [7105] 23547 23645 23743 23841 23939 24037 24135 24233 24331 24429 24527 24625
## [7117] 24723 24821 24919 25017 25115 25213 25311 25409 25507 25605 25703 25801
## [7129] 28 126 224 322 420 518 616 714 812 910 1008 1106
## [7141] 1204 1302 1400 1498 1596 1694 1792 1890 1988 2086 2184 2282
## [7153] 2380 2478 2576 2674 2772 2870 2968 3066 3164 3262 3360 3458
## [7165] 3556 3654 3752 3850 3948 4046 4144 4242 4340 4438 4536 4634
## [7177] 4732 4830 4928 5026 5124 5222 5320 5418 5516 5614 5712 5810
## [7189] 5908 6006 6104 6202 6300 6398 6496 6594 6692 6790 6888 6986
## [7201] 7084 7182 7280 7378 7476 7574 7672 7770 7868 7966 8064 8162
## [7213] 8260 8358 8456 8554 8652 8750 8848 8946 9044 9142 9240 9338
## [7225] 9436 9534 9632 9730 9828 9926 10024 10122 10220 10318 10416 10514
## [7237] 10612 10710 10808 10906 11004 11102 11200 11298 11396 11494 11592 11690
## [7249] 11788 11886 11984 12082 12180 12278 12376 12474 12572 12670 12768 12866
## [7261] 12964 13062 13160 13258 13356 13454 13552 13650 13748 13846 13944 14042
## [7273] 14140 14238 14336 14434 14532 14630 14728 14826 14924 15022 15120 15218
## [7285] 15316 15414 15512 15610 15708 15806 15904 16002 16100 16198 16296 16394
## [7297] 16492 16590 16688 16786 16884 16982 17080 17178 17276 17374 17472 17570
## [7309] 17668 17766 17864 17962 18060 18158 18256 18354 18452 18550 18648 18746
## [7321] 18844 18942 19040 19138 19236 19334 19432 19530 19628 19726 19824 19922
## [7333] 20020 20118 20216 20314 20412 20510 20608 20706 20804 20902 21000 21098
## [7345] 21196 21294 21392 21490 21588 21686 21784 21882 21980 22078 22176 22274
## [7357] 22372 22470 22568 22666 22764 22862 22960 23058 23156 23254 23352 23450
## [7369] 23548 23646 23744 23842 23940 24038 24136 24234 24332 24430 24528 24626
## [7381] 24724 24822 24920 25018 25116 25214 25312 25410 25508 25606 25704 25802
## [7393] 29 127 225 323 421 519 617 715 813 911 1009 1107
## [7405] 1205 1303 1401 1499 1597 1695 1793 1891 1989 2087 2185 2283
## [7417] 2381 2479 2577 2675 2773 2871 2969 3067 3165 3263 3361 3459
## [7429] 3557 3655 3753 3851 3949 4047 4145 4243 4341 4439 4537 4635
## [7441] 4733 4831 4929 5027 5125 5223 5321 5419 5517 5615 5713 5811
## [7453] 5909 6007 6105 6203 6301 6399 6497 6595 6693 6791 6889 6987
## [7465] 7085 7183 7281 7379 7477 7575 7673 7771 7869 7967 8065 8163
## [7477] 8261 8359 8457 8555 8653 8751 8849 8947 9045 9143 9241 9339
## [7489] 9437 9535 9633 9731 9829 9927 10025 10123 10221 10319 10417 10515
## [7501] 10613 10711 10809 10907 11005 11103 11201 11299 11397 11495 11593 11691
## [7513] 11789 11887 11985 12083 12181 12279 12377 12475 12573 12671 12769 12867
## [7525] 12965 13063 13161 13259 13357 13455 13553 13651 13749 13847 13945 14043
## [7537] 14141 14239 14337 14435 14533 14631 14729 14827 14925 15023 15121 15219
## [7549] 15317 15415 15513 15611 15709 15807 15905 16003 16101 16199 16297 16395
## [7561] 16493 16591 16689 16787 16885 16983 17081 17179 17277 17375 17473 17571
## [7573] 17669 17767 17865 17963 18061 18159 18257 18355 18453 18551 18649 18747
## [7585] 18845 18943 19041 19139 19237 19335 19433 19531 19629 19727 19825 19923
## [7597] 20021 20119 20217 20315 20413 20511 20609 20707 20805 20903 21001 21099
## [7609] 21197 21295 21393 21491 21589 21687 21785 21883 21981 22079 22177 22275
## [7621] 22373 22471 22569 22667 22765 22863 22961 23059 23157 23255 23353 23451
## [7633] 23549 23647 23745 23843 23941 24039 24137 24235 24333 24431 24529 24627
## [7645] 24725 24823 24921 25019 25117 25215 25313 25411 25509 25607 25705 25803
## [7657] 30 128 226 324 422 520 618 716 814 912 1010 1108
## [7669] 1206 1304 1402 1500 1598 1696 1794 1892 1990 2088 2186 2284
## [7681] 2382 2480 2578 2676 2774 2872 2970 3068 3166 3264 3362 3460
## [7693] 3558 3656 3754 3852 3950 4048 4146 4244 4342 4440 4538 4636
## [7705] 4734 4832 4930 5028 5126 5224 5322 5420 5518 5616 5714 5812
## [7717] 5910 6008 6106 6204 6302 6400 6498 6596 6694 6792 6890 6988
## [7729] 7086 7184 7282 7380 7478 7576 7674 7772 7870 7968 8066 8164
## [7741] 8262 8360 8458 8556 8654 8752 8850 8948 9046 9144 9242 9340
## [7753] 9438 9536 9634 9732 9830 9928 10026 10124 10222 10320 10418 10516
## [7765] 10614 10712 10810 10908 11006 11104 11202 11300 11398 11496 11594 11692
## [7777] 11790 11888 11986 12084 12182 12280 12378 12476 12574 12672 12770 12868
## [7789] 12966 13064 13162 13260 13358 13456 13554 13652 13750 13848 13946 14044
## [7801] 14142 14240 14338 14436 14534 14632 14730 14828 14926 15024 15122 15220
## [7813] 15318 15416 15514 15612 15710 15808 15906 16004 16102 16200 16298 16396
## [7825] 16494 16592 16690 16788 16886 16984 17082 17180 17278 17376 17474 17572
## [7837] 17670 17768 17866 17964 18062 18160 18258 18356 18454 18552 18650 18748
## [7849] 18846 18944 19042 19140 19238 19336 19434 19532 19630 19728 19826 19924
## [7861] 20022 20120 20218 20316 20414 20512 20610 20708 20806 20904 21002 21100
## [7873] 21198 21296 21394 21492 21590 21688 21786 21884 21982 22080 22178 22276
## [7885] 22374 22472 22570 22668 22766 22864 22962 23060 23158 23256 23354 23452
## [7897] 23550 23648 23746 23844 23942 24040 24138 24236 24334 24432 24530 24628
## [7909] 24726 24824 24922 25020 25118 25216 25314 25412 25510 25608 25706 25804
## [7921] 31 129 227 325 423 521 619 717 815 913 1011 1109
## [7933] 1207 1305 1403 1501 1599 1697 1795 1893 1991 2089 2187 2285
## [7945] 2383 2481 2579 2677 2775 2873 2971 3069 3167 3265 3363 3461
## [7957] 3559 3657 3755 3853 3951 4049 4147 4245 4343 4441 4539 4637
## [7969] 4735 4833 4931 5029 5127 5225 5323 5421 5519 5617 5715 5813
## [7981] 5911 6009 6107 6205 6303 6401 6499 6597 6695 6793 6891 6989
## [7993] 7087 7185 7283 7381 7479 7577 7675 7773 7871 7969 8067 8165
## [8005] 8263 8361 8459 8557 8655 8753 8851 8949 9047 9145 9243 9341
## [8017] 9439 9537 9635 9733 9831 9929 10027 10125 10223 10321 10419 10517
## [8029] 10615 10713 10811 10909 11007 11105 11203 11301 11399 11497 11595 11693
## [8041] 11791 11889 11987 12085 12183 12281 12379 12477 12575 12673 12771 12869
## [8053] 12967 13065 13163 13261 13359 13457 13555 13653 13751 13849 13947 14045
## [8065] 14143 14241 14339 14437 14535 14633 14731 14829 14927 15025 15123 15221
## [8077] 15319 15417 15515 15613 15711 15809 15907 16005 16103 16201 16299 16397
## [8089] 16495 16593 16691 16789 16887 16985 17083 17181 17279 17377 17475 17573
## [8101] 17671 17769 17867 17965 18063 18161 18259 18357 18455 18553 18651 18749
## [8113] 18847 18945 19043 19141 19239 19337 19435 19533 19631 19729 19827 19925
## [8125] 20023 20121 20219 20317 20415 20513 20611 20709 20807 20905 21003 21101
## [8137] 21199 21297 21395 21493 21591 21689 21787 21885 21983 22081 22179 22277
## [8149] 22375 22473 22571 22669 22767 22865 22963 23061 23159 23257 23355 23453
## [8161] 23551 23649 23747 23845 23943 24041 24139 24237 24335 24433 24531 24629
## [8173] 24727 24825 24923 25021 25119 25217 25315 25413 25511 25609 25707 25805
## [8185] 32 130 228 326 424 522 620 718 816 914 1012 1110
## [8197] 1208 1306 1404 1502 1600 1698 1796 1894 1992 2090 2188 2286
## [8209] 2384 2482 2580 2678 2776 2874 2972 3070 3168 3266 3364 3462
## [8221] 3560 3658 3756 3854 3952 4050 4148 4246 4344 4442 4540 4638
## [8233] 4736 4834 4932 5030 5128 5226 5324 5422 5520 5618 5716 5814
## [8245] 5912 6010 6108 6206 6304 6402 6500 6598 6696 6794 6892 6990
## [8257] 7088 7186 7284 7382 7480 7578 7676 7774 7872 7970 8068 8166
## [8269] 8264 8362 8460 8558 8656 8754 8852 8950 9048 9146 9244 9342
## [8281] 9440 9538 9636 9734 9832 9930 10028 10126 10224 10322 10420 10518
## [8293] 10616 10714 10812 10910 11008 11106 11204 11302 11400 11498 11596 11694
## [8305] 11792 11890 11988 12086 12184 12282 12380 12478 12576 12674 12772 12870
## [8317] 12968 13066 13164 13262 13360 13458 13556 13654 13752 13850 13948 14046
## [8329] 14144 14242 14340 14438 14536 14634 14732 14830 14928 15026 15124 15222
## [8341] 15320 15418 15516 15614 15712 15810 15908 16006 16104 16202 16300 16398
## [8353] 16496 16594 16692 16790 16888 16986 17084 17182 17280 17378 17476 17574
## [8365] 17672 17770 17868 17966 18064 18162 18260 18358 18456 18554 18652 18750
## [8377] 18848 18946 19044 19142 19240 19338 19436 19534 19632 19730 19828 19926
## [8389] 20024 20122 20220 20318 20416 20514 20612 20710 20808 20906 21004 21102
## [8401] 21200 21298 21396 21494 21592 21690 21788 21886 21984 22082 22180 22278
## [8413] 22376 22474 22572 22670 22768 22866 22964 23062 23160 23258 23356 23454
## [8425] 23552 23650 23748 23846 23944 24042 24140 24238 24336 24434 24532 24630
## [8437] 24728 24826 24924 25022 25120 25218 25316 25414 25512 25610 25708 25806
## [8449] 33 131 229 327 425 523 621 719 817 915 1013 1111
## [8461] 1209 1307 1405 1503 1601 1699 1797 1895 1993 2091 2189 2287
## [8473] 2385 2483 2581 2679 2777 2875 2973 3071 3169 3267 3365 3463
## [8485] 3561 3659 3757 3855 3953 4051 4149 4247 4345 4443 4541 4639
## [8497] 4737 4835 4933 5031 5129 5227 5325 5423 5521 5619 5717 5815
## [8509] 5913 6011 6109 6207 6305 6403 6501 6599 6697 6795 6893 6991
## [8521] 7089 7187 7285 7383 7481 7579 7677 7775 7873 7971 8069 8167
## [8533] 8265 8363 8461 8559 8657 8755 8853 8951 9049 9147 9245 9343
## [8545] 9441 9539 9637 9735 9833 9931 10029 10127 10225 10323 10421 10519
## [8557] 10617 10715 10813 10911 11009 11107 11205 11303 11401 11499 11597 11695
## [8569] 11793 11891 11989 12087 12185 12283 12381 12479 12577 12675 12773 12871
## [8581] 12969 13067 13165 13263 13361 13459 13557 13655 13753 13851 13949 14047
## [8593] 14145 14243 14341 14439 14537 14635 14733 14831 14929 15027 15125 15223
## [8605] 15321 15419 15517 15615 15713 15811 15909 16007 16105 16203 16301 16399
## [8617] 16497 16595 16693 16791 16889 16987 17085 17183 17281 17379 17477 17575
## [8629] 17673 17771 17869 17967 18065 18163 18261 18359 18457 18555 18653 18751
## [8641] 18849 18947 19045 19143 19241 19339 19437 19535 19633 19731 19829 19927
## [8653] 20025 20123 20221 20319 20417 20515 20613 20711 20809 20907 21005 21103
## [8665] 21201 21299 21397 21495 21593 21691 21789 21887 21985 22083 22181 22279
## [8677] 22377 22475 22573 22671 22769 22867 22965 23063 23161 23259 23357 23455
## [8689] 23553 23651 23749 23847 23945 24043 24141 24239 24337 24435 24533 24631
## [8701] 24729 24827 24925 25023 25121 25219 25317 25415 25513 25611 25709 25807
## [8713] 34 132 230 328 426 524 622 720 818 916 1014 1112
## [8725] 1210 1308 1406 1504 1602 1700 1798 1896 1994 2092 2190 2288
## [8737] 2386 2484 2582 2680 2778 2876 2974 3072 3170 3268 3366 3464
## [8749] 3562 3660 3758 3856 3954 4052 4150 4248 4346 4444 4542 4640
## [8761] 4738 4836 4934 5032 5130 5228 5326 5424 5522 5620 5718 5816
## [8773] 5914 6012 6110 6208 6306 6404 6502 6600 6698 6796 6894 6992
## [8785] 7090 7188 7286 7384 7482 7580 7678 7776 7874 7972 8070 8168
## [8797] 8266 8364 8462 8560 8658 8756 8854 8952 9050 9148 9246 9344
## [8809] 9442 9540 9638 9736 9834 9932 10030 10128 10226 10324 10422 10520
## [8821] 10618 10716 10814 10912 11010 11108 11206 11304 11402 11500 11598 11696
## [8833] 11794 11892 11990 12088 12186 12284 12382 12480 12578 12676 12774 12872
## [8845] 12970 13068 13166 13264 13362 13460 13558 13656 13754 13852 13950 14048
## [8857] 14146 14244 14342 14440 14538 14636 14734 14832 14930 15028 15126 15224
## [8869] 15322 15420 15518 15616 15714 15812 15910 16008 16106 16204 16302 16400
## [8881] 16498 16596 16694 16792 16890 16988 17086 17184 17282 17380 17478 17576
## [8893] 17674 17772 17870 17968 18066 18164 18262 18360 18458 18556 18654 18752
## [8905] 18850 18948 19046 19144 19242 19340 19438 19536 19634 19732 19830 19928
## [8917] 20026 20124 20222 20320 20418 20516 20614 20712 20810 20908 21006 21104
## [8929] 21202 21300 21398 21496 21594 21692 21790 21888 21986 22084 22182 22280
## [8941] 22378 22476 22574 22672 22770 22868 22966 23064 23162 23260 23358 23456
## [8953] 23554 23652 23750 23848 23946 24044 24142 24240 24338 24436 24534 24632
## [8965] 24730 24828 24926 25024 25122 25220 25318 25416 25514 25612 25710 25808
## [8977] 35 133 231 329 427 525 623 721 819 917 1015 1113
## [8989] 1211 1309 1407 1505 1603 1701 1799 1897 1995 2093 2191 2289
## [9001] 2387 2485 2583 2681 2779 2877 2975 3073 3171 3269 3367 3465
## [9013] 3563 3661 3759 3857 3955 4053 4151 4249 4347 4445 4543 4641
## [9025] 4739 4837 4935 5033 5131 5229 5327 5425 5523 5621 5719 5817
## [9037] 5915 6013 6111 6209 6307 6405 6503 6601 6699 6797 6895 6993
## [9049] 7091 7189 7287 7385 7483 7581 7679 7777 7875 7973 8071 8169
## [9061] 8267 8365 8463 8561 8659 8757 8855 8953 9051 9149 9247 9345
## [9073] 9443 9541 9639 9737 9835 9933 10031 10129 10227 10325 10423 10521
## [9085] 10619 10717 10815 10913 11011 11109 11207 11305 11403 11501 11599 11697
## [9097] 11795 11893 11991 12089 12187 12285 12383 12481 12579 12677 12775 12873
## [9109] 12971 13069 13167 13265 13363 13461 13559 13657 13755 13853 13951 14049
## [9121] 14147 14245 14343 14441 14539 14637 14735 14833 14931 15029 15127 15225
## [9133] 15323 15421 15519 15617 15715 15813 15911 16009 16107 16205 16303 16401
## [9145] 16499 16597 16695 16793 16891 16989 17087 17185 17283 17381 17479 17577
## [9157] 17675 17773 17871 17969 18067 18165 18263 18361 18459 18557 18655 18753
## [9169] 18851 18949 19047 19145 19243 19341 19439 19537 19635 19733 19831 19929
## [9181] 20027 20125 20223 20321 20419 20517 20615 20713 20811 20909 21007 21105
## [9193] 21203 21301 21399 21497 21595 21693 21791 21889 21987 22085 22183 22281
## [9205] 22379 22477 22575 22673 22771 22869 22967 23065 23163 23261 23359 23457
## [9217] 23555 23653 23751 23849 23947 24045 24143 24241 24339 24437 24535 24633
## [9229] 24731 24829 24927 25025 25123 25221 25319 25417 25515 25613 25711 25809
## [9241] 36 134 232 330 428 526 624 722 820 918 1016 1114
## [9253] 1212 1310 1408 1506 1604 1702 1800 1898 1996 2094 2192 2290
## [9265] 2388 2486 2584 2682 2780 2878 2976 3074 3172 3270 3368 3466
## [9277] 3564 3662 3760 3858 3956 4054 4152 4250 4348 4446 4544 4642
## [9289] 4740 4838 4936 5034 5132 5230 5328 5426 5524 5622 5720 5818
## [9301] 5916 6014 6112 6210 6308 6406 6504 6602 6700 6798 6896 6994
## [9313] 7092 7190 7288 7386 7484 7582 7680 7778 7876 7974 8072 8170
## [9325] 8268 8366 8464 8562 8660 8758 8856 8954 9052 9150 9248 9346
## [9337] 9444 9542 9640 9738 9836 9934 10032 10130 10228 10326 10424 10522
## [9349] 10620 10718 10816 10914 11012 11110 11208 11306 11404 11502 11600 11698
## [9361] 11796 11894 11992 12090 12188 12286 12384 12482 12580 12678 12776 12874
## [9373] 12972 13070 13168 13266 13364 13462 13560 13658 13756 13854 13952 14050
## [9385] 14148 14246 14344 14442 14540 14638 14736 14834 14932 15030 15128 15226
## [9397] 15324 15422 15520 15618 15716 15814 15912 16010 16108 16206 16304 16402
## [9409] 16500 16598 16696 16794 16892 16990 17088 17186 17284 17382 17480 17578
## [9421] 17676 17774 17872 17970 18068 18166 18264 18362 18460 18558 18656 18754
## [9433] 18852 18950 19048 19146 19244 19342 19440 19538 19636 19734 19832 19930
## [9445] 20028 20126 20224 20322 20420 20518 20616 20714 20812 20910 21008 21106
## [9457] 21204 21302 21400 21498 21596 21694 21792 21890 21988 22086 22184 22282
## [9469] 22380 22478 22576 22674 22772 22870 22968 23066 23164 23262 23360 23458
## [9481] 23556 23654 23752 23850 23948 24046 24144 24242 24340 24438 24536 24634
## [9493] 24732 24830 24928 25026 25124 25222 25320 25418 25516 25614 25712 25810
## [9505] 37 135 233 331 429 527 625 723 821 919 1017 1115
## [9517] 1213 1311 1409 1507 1605 1703 1801 1899 1997 2095 2193 2291
## [9529] 2389 2487 2585 2683 2781 2879 2977 3075 3173 3271 3369 3467
## [9541] 3565 3663 3761 3859 3957 4055 4153 4251 4349 4447 4545 4643
## [9553] 4741 4839 4937 5035 5133 5231 5329 5427 5525 5623 5721 5819
## [9565] 5917 6015 6113 6211 6309 6407 6505 6603 6701 6799 6897 6995
## [9577] 7093 7191 7289 7387 7485 7583 7681 7779 7877 7975 8073 8171
## [9589] 8269 8367 8465 8563 8661 8759 8857 8955 9053 9151 9249 9347
## [9601] 9445 9543 9641 9739 9837 9935 10033 10131 10229 10327 10425 10523
## [9613] 10621 10719 10817 10915 11013 11111 11209 11307 11405 11503 11601 11699
## [9625] 11797 11895 11993 12091 12189 12287 12385 12483 12581 12679 12777 12875
## [9637] 12973 13071 13169 13267 13365 13463 13561 13659 13757 13855 13953 14051
## [9649] 14149 14247 14345 14443 14541 14639 14737 14835 14933 15031 15129 15227
## [9661] 15325 15423 15521 15619 15717 15815 15913 16011 16109 16207 16305 16403
## [9673] 16501 16599 16697 16795 16893 16991 17089 17187 17285 17383 17481 17579
## [9685] 17677 17775 17873 17971 18069 18167 18265 18363 18461 18559 18657 18755
## [9697] 18853 18951 19049 19147 19245 19343 19441 19539 19637 19735 19833 19931
## [9709] 20029 20127 20225 20323 20421 20519 20617 20715 20813 20911 21009 21107
## [9721] 21205 21303 21401 21499 21597 21695 21793 21891 21989 22087 22185 22283
## [9733] 22381 22479 22577 22675 22773 22871 22969 23067 23165 23263 23361 23459
## [9745] 23557 23655 23753 23851 23949 24047 24145 24243 24341 24439 24537 24635
## [9757] 24733 24831 24929 25027 25125 25223 25321 25419 25517 25615 25713 25811
## [9769] 38 136 234 332 430 528 626 724 822 920 1018 1116
## [9781] 1214 1312 1410 1508 1606 1704 1802 1900 1998 2096 2194 2292
## [9793] 2390 2488 2586 2684 2782 2880 2978 3076 3174 3272 3370 3468
## [9805] 3566 3664 3762 3860 3958 4056 4154 4252 4350 4448 4546 4644
## [9817] 4742 4840 4938 5036 5134 5232 5330 5428 5526 5624 5722 5820
## [9829] 5918 6016 6114 6212 6310 6408 6506 6604 6702 6800 6898 6996
## [9841] 7094 7192 7290 7388 7486 7584 7682 7780 7878 7976 8074 8172
## [9853] 8270 8368 8466 8564 8662 8760 8858 8956 9054 9152 9250 9348
## [9865] 9446 9544 9642 9740 9838 9936 10034 10132 10230 10328 10426 10524
## [9877] 10622 10720 10818 10916 11014 11112 11210 11308 11406 11504 11602 11700
## [9889] 11798 11896 11994 12092 12190 12288 12386 12484 12582 12680 12778 12876
## [9901] 12974 13072 13170 13268 13366 13464 13562 13660 13758 13856 13954 14052
## [9913] 14150 14248 14346 14444 14542 14640 14738 14836 14934 15032 15130 15228
## [9925] 15326 15424 15522 15620 15718 15816 15914 16012 16110 16208 16306 16404
## [9937] 16502 16600 16698 16796 16894 16992 17090 17188 17286 17384 17482 17580
## [9949] 17678 17776 17874 17972 18070 18168 18266 18364 18462 18560 18658 18756
## [9961] 18854 18952 19050 19148 19246 19344 19442 19540 19638 19736 19834 19932
## [9973] 20030 20128 20226 20324 20422 20520 20618 20716 20814 20912 21010 21108
## [9985] 21206 21304 21402 21500 21598 21696 21794 21892 21990 22088 22186 22284
## [9997] 22382 22480 22578 22676 22774 22872 22970 23068 23166 23264 23362 23460
## [10009] 23558 23656 23754 23852 23950 24048 24146 24244 24342 24440 24538 24636
## [10021] 24734 24832 24930 25028 25126 25224 25322 25420 25518 25616 25714 25812
## [10033] 39 137 235 333 431 529 627 725 823 921 1019 1117
## [10045] 1215 1313 1411 1509 1607 1705 1803 1901 1999 2097 2195 2293
## [10057] 2391 2489 2587 2685 2783 2881 2979 3077 3175 3273 3371 3469
## [10069] 3567 3665 3763 3861 3959 4057 4155 4253 4351 4449 4547 4645
## [10081] 4743 4841 4939 5037 5135 5233 5331 5429 5527 5625 5723 5821
## [10093] 5919 6017 6115 6213 6311 6409 6507 6605 6703 6801 6899 6997
## [10105] 7095 7193 7291 7389 7487 7585 7683 7781 7879 7977 8075 8173
## [10117] 8271 8369 8467 8565 8663 8761 8859 8957 9055 9153 9251 9349
## [10129] 9447 9545 9643 9741 9839 9937 10035 10133 10231 10329 10427 10525
## [10141] 10623 10721 10819 10917 11015 11113 11211 11309 11407 11505 11603 11701
## [10153] 11799 11897 11995 12093 12191 12289 12387 12485 12583 12681 12779 12877
## [10165] 12975 13073 13171 13269 13367 13465 13563 13661 13759 13857 13955 14053
## [10177] 14151 14249 14347 14445 14543 14641 14739 14837 14935 15033 15131 15229
## [10189] 15327 15425 15523 15621 15719 15817 15915 16013 16111 16209 16307 16405
## [10201] 16503 16601 16699 16797 16895 16993 17091 17189 17287 17385 17483 17581
## [10213] 17679 17777 17875 17973 18071 18169 18267 18365 18463 18561 18659 18757
## [10225] 18855 18953 19051 19149 19247 19345 19443 19541 19639 19737 19835 19933
## [10237] 20031 20129 20227 20325 20423 20521 20619 20717 20815 20913 21011 21109
## [10249] 21207 21305 21403 21501 21599 21697 21795 21893 21991 22089 22187 22285
## [10261] 22383 22481 22579 22677 22775 22873 22971 23069 23167 23265 23363 23461
## [10273] 23559 23657 23755 23853 23951 24049 24147 24245 24343 24441 24539 24637
## [10285] 24735 24833 24931 25029 25127 25225 25323 25421 25519 25617 25715 25813
## [10297] 40 138 236 334 432 530 628 726 824 922 1020 1118
## [10309] 1216 1314 1412 1510 1608 1706 1804 1902 2000 2098 2196 2294
## [10321] 2392 2490 2588 2686 2784 2882 2980 3078 3176 3274 3372 3470
## [10333] 3568 3666 3764 3862 3960 4058 4156 4254 4352 4450 4548 4646
## [10345] 4744 4842 4940 5038 5136 5234 5332 5430 5528 5626 5724 5822
## [10357] 5920 6018 6116 6214 6312 6410 6508 6606 6704 6802 6900 6998
## [10369] 7096 7194 7292 7390 7488 7586 7684 7782 7880 7978 8076 8174
## [10381] 8272 8370 8468 8566 8664 8762 8860 8958 9056 9154 9252 9350
## [10393] 9448 9546 9644 9742 9840 9938 10036 10134 10232 10330 10428 10526
## [10405] 10624 10722 10820 10918 11016 11114 11212 11310 11408 11506 11604 11702
## [10417] 11800 11898 11996 12094 12192 12290 12388 12486 12584 12682 12780 12878
## [10429] 12976 13074 13172 13270 13368 13466 13564 13662 13760 13858 13956 14054
## [10441] 14152 14250 14348 14446 14544 14642 14740 14838 14936 15034 15132 15230
## [10453] 15328 15426 15524 15622 15720 15818 15916 16014 16112 16210 16308 16406
## [10465] 16504 16602 16700 16798 16896 16994 17092 17190 17288 17386 17484 17582
## [10477] 17680 17778 17876 17974 18072 18170 18268 18366 18464 18562 18660 18758
## [10489] 18856 18954 19052 19150 19248 19346 19444 19542 19640 19738 19836 19934
## [10501] 20032 20130 20228 20326 20424 20522 20620 20718 20816 20914 21012 21110
## [10513] 21208 21306 21404 21502 21600 21698 21796 21894 21992 22090 22188 22286
## [10525] 22384 22482 22580 22678 22776 22874 22972 23070 23168 23266 23364 23462
## [10537] 23560 23658 23756 23854 23952 24050 24148 24246 24344 24442 24540 24638
## [10549] 24736 24834 24932 25030 25128 25226 25324 25422 25520 25618 25716 25814
## [10561] 41 139 237 335 433 531 629 727 825 923 1021 1119
## [10573] 1217 1315 1413 1511 1609 1707 1805 1903 2001 2099 2197 2295
## [10585] 2393 2491 2589 2687 2785 2883 2981 3079 3177 3275 3373 3471
## [10597] 3569 3667 3765 3863 3961 4059 4157 4255 4353 4451 4549 4647
## [10609] 4745 4843 4941 5039 5137 5235 5333 5431 5529 5627 5725 5823
## [10621] 5921 6019 6117 6215 6313 6411 6509 6607 6705 6803 6901 6999
## [10633] 7097 7195 7293 7391 7489 7587 7685 7783 7881 7979 8077 8175
## [10645] 8273 8371 8469 8567 8665 8763 8861 8959 9057 9155 9253 9351
## [10657] 9449 9547 9645 9743 9841 9939 10037 10135 10233 10331 10429 10527
## [10669] 10625 10723 10821 10919 11017 11115 11213 11311 11409 11507 11605 11703
## [10681] 11801 11899 11997 12095 12193 12291 12389 12487 12585 12683 12781 12879
## [10693] 12977 13075 13173 13271 13369 13467 13565 13663 13761 13859 13957 14055
## [10705] 14153 14251 14349 14447 14545 14643 14741 14839 14937 15035 15133 15231
## [10717] 15329 15427 15525 15623 15721 15819 15917 16015 16113 16211 16309 16407
## [10729] 16505 16603 16701 16799 16897 16995 17093 17191 17289 17387 17485 17583
## [10741] 17681 17779 17877 17975 18073 18171 18269 18367 18465 18563 18661 18759
## [10753] 18857 18955 19053 19151 19249 19347 19445 19543 19641 19739 19837 19935
## [10765] 20033 20131 20229 20327 20425 20523 20621 20719 20817 20915 21013 21111
## [10777] 21209 21307 21405 21503 21601 21699 21797 21895 21993 22091 22189 22287
## [10789] 22385 22483 22581 22679 22777 22875 22973 23071 23169 23267 23365 23463
## [10801] 23561 23659 23757 23855 23953 24051 24149 24247 24345 24443 24541 24639
## [10813] 24737 24835 24933 25031 25129 25227 25325 25423 25521 25619 25717 25815
## [10825] 42 140 238 336 434 532 630 728 826 924 1022 1120
## [10837] 1218 1316 1414 1512 1610 1708 1806 1904 2002 2100 2198 2296
## [10849] 2394 2492 2590 2688 2786 2884 2982 3080 3178 3276 3374 3472
## [10861] 3570 3668 3766 3864 3962 4060 4158 4256 4354 4452 4550 4648
## [10873] 4746 4844 4942 5040 5138 5236 5334 5432 5530 5628 5726 5824
## [10885] 5922 6020 6118 6216 6314 6412 6510 6608 6706 6804 6902 7000
## [10897] 7098 7196 7294 7392 7490 7588 7686 7784 7882 7980 8078 8176
## [10909] 8274 8372 8470 8568 8666 8764 8862 8960 9058 9156 9254 9352
## [10921] 9450 9548 9646 9744 9842 9940 10038 10136 10234 10332 10430 10528
## [10933] 10626 10724 10822 10920 11018 11116 11214 11312 11410 11508 11606 11704
## [10945] 11802 11900 11998 12096 12194 12292 12390 12488 12586 12684 12782 12880
## [10957] 12978 13076 13174 13272 13370 13468 13566 13664 13762 13860 13958 14056
## [10969] 14154 14252 14350 14448 14546 14644 14742 14840 14938 15036 15134 15232
## [10981] 15330 15428 15526 15624 15722 15820 15918 16016 16114 16212 16310 16408
## [10993] 16506 16604 16702 16800 16898 16996 17094 17192 17290 17388 17486 17584
## [11005] 17682 17780 17878 17976 18074 18172 18270 18368 18466 18564 18662 18760
## [11017] 18858 18956 19054 19152 19250 19348 19446 19544 19642 19740 19838 19936
## [11029] 20034 20132 20230 20328 20426 20524 20622 20720 20818 20916 21014 21112
## [11041] 21210 21308 21406 21504 21602 21700 21798 21896 21994 22092 22190 22288
## [11053] 22386 22484 22582 22680 22778 22876 22974 23072 23170 23268 23366 23464
## [11065] 23562 23660 23758 23856 23954 24052 24150 24248 24346 24444 24542 24640
## [11077] 24738 24836 24934 25032 25130 25228 25326 25424 25522 25620 25718 25816
## [11089] 43 141 239 337 435 533 631 729 827 925 1023 1121
## [11101] 1219 1317 1415 1513 1611 1709 1807 1905 2003 2101 2199 2297
## [11113] 2395 2493 2591 2689 2787 2885 2983 3081 3179 3277 3375 3473
## [11125] 3571 3669 3767 3865 3963 4061 4159 4257 4355 4453 4551 4649
## [11137] 4747 4845 4943 5041 5139 5237 5335 5433 5531 5629 5727 5825
## [11149] 5923 6021 6119 6217 6315 6413 6511 6609 6707 6805 6903 7001
## [11161] 7099 7197 7295 7393 7491 7589 7687 7785 7883 7981 8079 8177
## [11173] 8275 8373 8471 8569 8667 8765 8863 8961 9059 9157 9255 9353
## [11185] 9451 9549 9647 9745 9843 9941 10039 10137 10235 10333 10431 10529
## [11197] 10627 10725 10823 10921 11019 11117 11215 11313 11411 11509 11607 11705
## [11209] 11803 11901 11999 12097 12195 12293 12391 12489 12587 12685 12783 12881
## [11221] 12979 13077 13175 13273 13371 13469 13567 13665 13763 13861 13959 14057
## [11233] 14155 14253 14351 14449 14547 14645 14743 14841 14939 15037 15135 15233
## [11245] 15331 15429 15527 15625 15723 15821 15919 16017 16115 16213 16311 16409
## [11257] 16507 16605 16703 16801 16899 16997 17095 17193 17291 17389 17487 17585
## [11269] 17683 17781 17879 17977 18075 18173 18271 18369 18467 18565 18663 18761
## [11281] 18859 18957 19055 19153 19251 19349 19447 19545 19643 19741 19839 19937
## [11293] 20035 20133 20231 20329 20427 20525 20623 20721 20819 20917 21015 21113
## [11305] 21211 21309 21407 21505 21603 21701 21799 21897 21995 22093 22191 22289
## [11317] 22387 22485 22583 22681 22779 22877 22975 23073 23171 23269 23367 23465
## [11329] 23563 23661 23759 23857 23955 24053 24151 24249 24347 24445 24543 24641
## [11341] 24739 24837 24935 25033 25131 25229 25327 25425 25523 25621 25719 25817
## [11353] 44 142 240 338 436 534 632 730 828 926 1024 1122
## [11365] 1220 1318 1416 1514 1612 1710 1808 1906 2004 2102 2200 2298
## [11377] 2396 2494 2592 2690 2788 2886 2984 3082 3180 3278 3376 3474
## [11389] 3572 3670 3768 3866 3964 4062 4160 4258 4356 4454 4552 4650
## [11401] 4748 4846 4944 5042 5140 5238 5336 5434 5532 5630 5728 5826
## [11413] 5924 6022 6120 6218 6316 6414 6512 6610 6708 6806 6904 7002
## [11425] 7100 7198 7296 7394 7492 7590 7688 7786 7884 7982 8080 8178
## [11437] 8276 8374 8472 8570 8668 8766 8864 8962 9060 9158 9256 9354
## [11449] 9452 9550 9648 9746 9844 9942 10040 10138 10236 10334 10432 10530
## [11461] 10628 10726 10824 10922 11020 11118 11216 11314 11412 11510 11608 11706
## [11473] 11804 11902 12000 12098 12196 12294 12392 12490 12588 12686 12784 12882
## [11485] 12980 13078 13176 13274 13372 13470 13568 13666 13764 13862 13960 14058
## [11497] 14156 14254 14352 14450 14548 14646 14744 14842 14940 15038 15136 15234
## [11509] 15332 15430 15528 15626 15724 15822 15920 16018 16116 16214 16312 16410
## [11521] 16508 16606 16704 16802 16900 16998 17096 17194 17292 17390 17488 17586
## [11533] 17684 17782 17880 17978 18076 18174 18272 18370 18468 18566 18664 18762
## [11545] 18860 18958 19056 19154 19252 19350 19448 19546 19644 19742 19840 19938
## [11557] 20036 20134 20232 20330 20428 20526 20624 20722 20820 20918 21016 21114
## [11569] 21212 21310 21408 21506 21604 21702 21800 21898 21996 22094 22192 22290
## [11581] 22388 22486 22584 22682 22780 22878 22976 23074 23172 23270 23368 23466
## [11593] 23564 23662 23760 23858 23956 24054 24152 24250 24348 24446 24544 24642
## [11605] 24740 24838 24936 25034 25132 25230 25328 25426 25524 25622 25720 25818
## [11617] 45 143 241 339 437 535 633 731 829 927 1025 1123
## [11629] 1221 1319 1417 1515 1613 1711 1809 1907 2005 2103 2201 2299
## [11641] 2397 2495 2593 2691 2789 2887 2985 3083 3181 3279 3377 3475
## [11653] 3573 3671 3769 3867 3965 4063 4161 4259 4357 4455 4553 4651
## [11665] 4749 4847 4945 5043 5141 5239 5337 5435 5533 5631 5729 5827
## [11677] 5925 6023 6121 6219 6317 6415 6513 6611 6709 6807 6905 7003
## [11689] 7101 7199 7297 7395 7493 7591 7689 7787 7885 7983 8081 8179
## [11701] 8277 8375 8473 8571 8669 8767 8865 8963 9061 9159 9257 9355
## [11713] 9453 9551 9649 9747 9845 9943 10041 10139 10237 10335 10433 10531
## [11725] 10629 10727 10825 10923 11021 11119 11217 11315 11413 11511 11609 11707
## [11737] 11805 11903 12001 12099 12197 12295 12393 12491 12589 12687 12785 12883
## [11749] 12981 13079 13177 13275 13373 13471 13569 13667 13765 13863 13961 14059
## [11761] 14157 14255 14353 14451 14549 14647 14745 14843 14941 15039 15137 15235
## [11773] 15333 15431 15529 15627 15725 15823 15921 16019 16117 16215 16313 16411
## [11785] 16509 16607 16705 16803 16901 16999 17097 17195 17293 17391 17489 17587
## [11797] 17685 17783 17881 17979 18077 18175 18273 18371 18469 18567 18665 18763
## [11809] 18861 18959 19057 19155 19253 19351 19449 19547 19645 19743 19841 19939
## [11821] 20037 20135 20233 20331 20429 20527 20625 20723 20821 20919 21017 21115
## [11833] 21213 21311 21409 21507 21605 21703 21801 21899 21997 22095 22193 22291
## [11845] 22389 22487 22585 22683 22781 22879 22977 23075 23173 23271 23369 23467
## [11857] 23565 23663 23761 23859 23957 24055 24153 24251 24349 24447 24545 24643
## [11869] 24741 24839 24937 25035 25133 25231 25329 25427 25525 25623 25721 25819
## [11881] 46 144 242 340 438 536 634 732 830 928 1026 1124
## [11893] 1222 1320 1418 1516 1614 1712 1810 1908 2006 2104 2202 2300
## [11905] 2398 2496 2594 2692 2790 2888 2986 3084 3182 3280 3378 3476
## [11917] 3574 3672 3770 3868 3966 4064 4162 4260 4358 4456 4554 4652
## [11929] 4750 4848 4946 5044 5142 5240 5338 5436 5534 5632 5730 5828
## [11941] 5926 6024 6122 6220 6318 6416 6514 6612 6710 6808 6906 7004
## [11953] 7102 7200 7298 7396 7494 7592 7690 7788 7886 7984 8082 8180
## [11965] 8278 8376 8474 8572 8670 8768 8866 8964 9062 9160 9258 9356
## [11977] 9454 9552 9650 9748 9846 9944 10042 10140 10238 10336 10434 10532
## [11989] 10630 10728 10826 10924 11022 11120 11218 11316 11414 11512 11610 11708
## [12001] 11806 11904 12002 12100 12198 12296 12394 12492 12590 12688 12786 12884
## [12013] 12982 13080 13178 13276 13374 13472 13570 13668 13766 13864 13962 14060
## [12025] 14158 14256 14354 14452 14550 14648 14746 14844 14942 15040 15138 15236
## [12037] 15334 15432 15530 15628 15726 15824 15922 16020 16118 16216 16314 16412
## [12049] 16510 16608 16706 16804 16902 17000 17098 17196 17294 17392 17490 17588
## [12061] 17686 17784 17882 17980 18078 18176 18274 18372 18470 18568 18666 18764
## [12073] 18862 18960 19058 19156 19254 19352 19450 19548 19646 19744 19842 19940
## [12085] 20038 20136 20234 20332 20430 20528 20626 20724 20822 20920 21018 21116
## [12097] 21214 21312 21410 21508 21606 21704 21802 21900 21998 22096 22194 22292
## [12109] 22390 22488 22586 22684 22782 22880 22978 23076 23174 23272 23370 23468
## [12121] 23566 23664 23762 23860 23958 24056 24154 24252 24350 24448 24546 24644
## [12133] 24742 24840 24938 25036 25134 25232 25330 25428 25526 25624 25722 25820
## [12145] 47 145 243 341 439 537 635 733 831 929 1027 1125
## [12157] 1223 1321 1419 1517 1615 1713 1811 1909 2007 2105 2203 2301
## [12169] 2399 2497 2595 2693 2791 2889 2987 3085 3183 3281 3379 3477
## [12181] 3575 3673 3771 3869 3967 4065 4163 4261 4359 4457 4555 4653
## [12193] 4751 4849 4947 5045 5143 5241 5339 5437 5535 5633 5731 5829
## [12205] 5927 6025 6123 6221 6319 6417 6515 6613 6711 6809 6907 7005
## [12217] 7103 7201 7299 7397 7495 7593 7691 7789 7887 7985 8083 8181
## [12229] 8279 8377 8475 8573 8671 8769 8867 8965 9063 9161 9259 9357
## [12241] 9455 9553 9651 9749 9847 9945 10043 10141 10239 10337 10435 10533
## [12253] 10631 10729 10827 10925 11023 11121 11219 11317 11415 11513 11611 11709
## [12265] 11807 11905 12003 12101 12199 12297 12395 12493 12591 12689 12787 12885
## [12277] 12983 13081 13179 13277 13375 13473 13571 13669 13767 13865 13963 14061
## [12289] 14159 14257 14355 14453 14551 14649 14747 14845 14943 15041 15139 15237
## [12301] 15335 15433 15531 15629 15727 15825 15923 16021 16119 16217 16315 16413
## [12313] 16511 16609 16707 16805 16903 17001 17099 17197 17295 17393 17491 17589
## [12325] 17687 17785 17883 17981 18079 18177 18275 18373 18471 18569 18667 18765
## [12337] 18863 18961 19059 19157 19255 19353 19451 19549 19647 19745 19843 19941
## [12349] 20039 20137 20235 20333 20431 20529 20627 20725 20823 20921 21019 21117
## [12361] 21215 21313 21411 21509 21607 21705 21803 21901 21999 22097 22195 22293
## [12373] 22391 22489 22587 22685 22783 22881 22979 23077 23175 23273 23371 23469
## [12385] 23567 23665 23763 23861 23959 24057 24155 24253 24351 24449 24547 24645
## [12397] 24743 24841 24939 25037 25135 25233 25331 25429 25527 25625 25723 25821
## [12409] 48 146 244 342 440 538 636 734 832 930 1028 1126
## [12421] 1224 1322 1420 1518 1616 1714 1812 1910 2008 2106 2204 2302
## [12433] 2400 2498 2596 2694 2792 2890 2988 3086 3184 3282 3380 3478
## [12445] 3576 3674 3772 3870 3968 4066 4164 4262 4360 4458 4556 4654
## [12457] 4752 4850 4948 5046 5144 5242 5340 5438 5536 5634 5732 5830
## [12469] 5928 6026 6124 6222 6320 6418 6516 6614 6712 6810 6908 7006
## [12481] 7104 7202 7300 7398 7496 7594 7692 7790 7888 7986 8084 8182
## [12493] 8280 8378 8476 8574 8672 8770 8868 8966 9064 9162 9260 9358
## [12505] 9456 9554 9652 9750 9848 9946 10044 10142 10240 10338 10436 10534
## [12517] 10632 10730 10828 10926 11024 11122 11220 11318 11416 11514 11612 11710
## [12529] 11808 11906 12004 12102 12200 12298 12396 12494 12592 12690 12788 12886
## [12541] 12984 13082 13180 13278 13376 13474 13572 13670 13768 13866 13964 14062
## [12553] 14160 14258 14356 14454 14552 14650 14748 14846 14944 15042 15140 15238
## [12565] 15336 15434 15532 15630 15728 15826 15924 16022 16120 16218 16316 16414
## [12577] 16512 16610 16708 16806 16904 17002 17100 17198 17296 17394 17492 17590
## [12589] 17688 17786 17884 17982 18080 18178 18276 18374 18472 18570 18668 18766
## [12601] 18864 18962 19060 19158 19256 19354 19452 19550 19648 19746 19844 19942
## [12613] 20040 20138 20236 20334 20432 20530 20628 20726 20824 20922 21020 21118
## [12625] 21216 21314 21412 21510 21608 21706 21804 21902 22000 22098 22196 22294
## [12637] 22392 22490 22588 22686 22784 22882 22980 23078 23176 23274 23372 23470
## [12649] 23568 23666 23764 23862 23960 24058 24156 24254 24352 24450 24548 24646
## [12661] 24744 24842 24940 25038 25136 25234 25332 25430 25528 25626 25724 25822
## [12673] 49 147 245 343 441 539 637 735 833 931 1029 1127
## [12685] 1225 1323 1421 1519 1617 1715 1813 1911 2009 2107 2205 2303
## [12697] 2401 2499 2597 2695 2793 2891 2989 3087 3185 3283 3381 3479
## [12709] 3577 3675 3773 3871 3969 4067 4165 4263 4361 4459 4557 4655
## [12721] 4753 4851 4949 5047 5145 5243 5341 5439 5537 5635 5733 5831
## [12733] 5929 6027 6125 6223 6321 6419 6517 6615 6713 6811 6909 7007
## [12745] 7105 7203 7301 7399 7497 7595 7693 7791 7889 7987 8085 8183
## [12757] 8281 8379 8477 8575 8673 8771 8869 8967 9065 9163 9261 9359
## [12769] 9457 9555 9653 9751 9849 9947 10045 10143 10241 10339 10437 10535
## [12781] 10633 10731 10829 10927 11025 11123 11221 11319 11417 11515 11613 11711
## [12793] 11809 11907 12005 12103 12201 12299 12397 12495 12593 12691 12789 12887
## [12805] 12985 13083 13181 13279 13377 13475 13573 13671 13769 13867 13965 14063
## [12817] 14161 14259 14357 14455 14553 14651 14749 14847 14945 15043 15141 15239
## [12829] 15337 15435 15533 15631 15729 15827 15925 16023 16121 16219 16317 16415
## [12841] 16513 16611 16709 16807 16905 17003 17101 17199 17297 17395 17493 17591
## [12853] 17689 17787 17885 17983 18081 18179 18277 18375 18473 18571 18669 18767
## [12865] 18865 18963 19061 19159 19257 19355 19453 19551 19649 19747 19845 19943
## [12877] 20041 20139 20237 20335 20433 20531 20629 20727 20825 20923 21021 21119
## [12889] 21217 21315 21413 21511 21609 21707 21805 21903 22001 22099 22197 22295
## [12901] 22393 22491 22589 22687 22785 22883 22981 23079 23177 23275 23373 23471
## [12913] 23569 23667 23765 23863 23961 24059 24157 24255 24353 24451 24549 24647
## [12925] 24745 24843 24941 25039 25137 25235 25333 25431 25529 25627 25725 25823
## [12937] 50 148 246 344 442 540 638 736 834 932 1030 1128
## [12949] 1226 1324 1422 1520 1618 1716 1814 1912 2010 2108 2206 2304
## [12961] 2402 2500 2598 2696 2794 2892 2990 3088 3186 3284 3382 3480
## [12973] 3578 3676 3774 3872 3970 4068 4166 4264 4362 4460 4558 4656
## [12985] 4754 4852 4950 5048 5146 5244 5342 5440 5538 5636 5734 5832
## [12997] 5930 6028 6126 6224 6322 6420 6518 6616 6714 6812 6910 7008
## [13009] 7106 7204 7302 7400 7498 7596 7694 7792 7890 7988 8086 8184
## [13021] 8282 8380 8478 8576 8674 8772 8870 8968 9066 9164 9262 9360
## [13033] 9458 9556 9654 9752 9850 9948 10046 10144 10242 10340 10438 10536
## [13045] 10634 10732 10830 10928 11026 11124 11222 11320 11418 11516 11614 11712
## [13057] 11810 11908 12006 12104 12202 12300 12398 12496 12594 12692 12790 12888
## [13069] 12986 13084 13182 13280 13378 13476 13574 13672 13770 13868 13966 14064
## [13081] 14162 14260 14358 14456 14554 14652 14750 14848 14946 15044 15142 15240
## [13093] 15338 15436 15534 15632 15730 15828 15926 16024 16122 16220 16318 16416
## [13105] 16514 16612 16710 16808 16906 17004 17102 17200 17298 17396 17494 17592
## [13117] 17690 17788 17886 17984 18082 18180 18278 18376 18474 18572 18670 18768
## [13129] 18866 18964 19062 19160 19258 19356 19454 19552 19650 19748 19846 19944
## [13141] 20042 20140 20238 20336 20434 20532 20630 20728 20826 20924 21022 21120
## [13153] 21218 21316 21414 21512 21610 21708 21806 21904 22002 22100 22198 22296
## [13165] 22394 22492 22590 22688 22786 22884 22982 23080 23178 23276 23374 23472
## [13177] 23570 23668 23766 23864 23962 24060 24158 24256 24354 24452 24550 24648
## [13189] 24746 24844 24942 25040 25138 25236 25334 25432 25530 25628 25726 25824
## [13201] 51 149 247 345 443 541 639 737 835 933 1031 1129
## [13213] 1227 1325 1423 1521 1619 1717 1815 1913 2011 2109 2207 2305
## [13225] 2403 2501 2599 2697 2795 2893 2991 3089 3187 3285 3383 3481
## [13237] 3579 3677 3775 3873 3971 4069 4167 4265 4363 4461 4559 4657
## [13249] 4755 4853 4951 5049 5147 5245 5343 5441 5539 5637 5735 5833
## [13261] 5931 6029 6127 6225 6323 6421 6519 6617 6715 6813 6911 7009
## [13273] 7107 7205 7303 7401 7499 7597 7695 7793 7891 7989 8087 8185
## [13285] 8283 8381 8479 8577 8675 8773 8871 8969 9067 9165 9263 9361
## [13297] 9459 9557 9655 9753 9851 9949 10047 10145 10243 10341 10439 10537
## [13309] 10635 10733 10831 10929 11027 11125 11223 11321 11419 11517 11615 11713
## [13321] 11811 11909 12007 12105 12203 12301 12399 12497 12595 12693 12791 12889
## [13333] 12987 13085 13183 13281 13379 13477 13575 13673 13771 13869 13967 14065
## [13345] 14163 14261 14359 14457 14555 14653 14751 14849 14947 15045 15143 15241
## [13357] 15339 15437 15535 15633 15731 15829 15927 16025 16123 16221 16319 16417
## [13369] 16515 16613 16711 16809 16907 17005 17103 17201 17299 17397 17495 17593
## [13381] 17691 17789 17887 17985 18083 18181 18279 18377 18475 18573 18671 18769
## [13393] 18867 18965 19063 19161 19259 19357 19455 19553 19651 19749 19847 19945
## [13405] 20043 20141 20239 20337 20435 20533 20631 20729 20827 20925 21023 21121
## [13417] 21219 21317 21415 21513 21611 21709 21807 21905 22003 22101 22199 22297
## [13429] 22395 22493 22591 22689 22787 22885 22983 23081 23179 23277 23375 23473
## [13441] 23571 23669 23767 23865 23963 24061 24159 24257 24355 24453 24551 24649
## [13453] 24747 24845 24943 25041 25139 25237 25335 25433 25531 25629 25727 25825
## [13465] 52 150 248 346 444 542 640 738 836 934 1032 1130
## [13477] 1228 1326 1424 1522 1620 1718 1816 1914 2012 2110 2208 2306
## [13489] 2404 2502 2600 2698 2796 2894 2992 3090 3188 3286 3384 3482
## [13501] 3580 3678 3776 3874 3972 4070 4168 4266 4364 4462 4560 4658
## [13513] 4756 4854 4952 5050 5148 5246 5344 5442 5540 5638 5736 5834
## [13525] 5932 6030 6128 6226 6324 6422 6520 6618 6716 6814 6912 7010
## [13537] 7108 7206 7304 7402 7500 7598 7696 7794 7892 7990 8088 8186
## [13549] 8284 8382 8480 8578 8676 8774 8872 8970 9068 9166 9264 9362
## [13561] 9460 9558 9656 9754 9852 9950 10048 10146 10244 10342 10440 10538
## [13573] 10636 10734 10832 10930 11028 11126 11224 11322 11420 11518 11616 11714
## [13585] 11812 11910 12008 12106 12204 12302 12400 12498 12596 12694 12792 12890
## [13597] 12988 13086 13184 13282 13380 13478 13576 13674 13772 13870 13968 14066
## [13609] 14164 14262 14360 14458 14556 14654 14752 14850 14948 15046 15144 15242
## [13621] 15340 15438 15536 15634 15732 15830 15928 16026 16124 16222 16320 16418
## [13633] 16516 16614 16712 16810 16908 17006 17104 17202 17300 17398 17496 17594
## [13645] 17692 17790 17888 17986 18084 18182 18280 18378 18476 18574 18672 18770
## [13657] 18868 18966 19064 19162 19260 19358 19456 19554 19652 19750 19848 19946
## [13669] 20044 20142 20240 20338 20436 20534 20632 20730 20828 20926 21024 21122
## [13681] 21220 21318 21416 21514 21612 21710 21808 21906 22004 22102 22200 22298
## [13693] 22396 22494 22592 22690 22788 22886 22984 23082 23180 23278 23376 23474
## [13705] 23572 23670 23768 23866 23964 24062 24160 24258 24356 24454 24552 24650
## [13717] 24748 24846 24944 25042 25140 25238 25336 25434 25532 25630 25728 25826
## [13729] 53 151 249 347 445 543 641 739 837 935 1033 1131
## [13741] 1229 1327 1425 1523 1621 1719 1817 1915 2013 2111 2209 2307
## [13753] 2405 2503 2601 2699 2797 2895 2993 3091 3189 3287 3385 3483
## [13765] 3581 3679 3777 3875 3973 4071 4169 4267 4365 4463 4561 4659
## [13777] 4757 4855 4953 5051 5149 5247 5345 5443 5541 5639 5737 5835
## [13789] 5933 6031 6129 6227 6325 6423 6521 6619 6717 6815 6913 7011
## [13801] 7109 7207 7305 7403 7501 7599 7697 7795 7893 7991 8089 8187
## [13813] 8285 8383 8481 8579 8677 8775 8873 8971 9069 9167 9265 9363
## [13825] 9461 9559 9657 9755 9853 9951 10049 10147 10245 10343 10441 10539
## [13837] 10637 10735 10833 10931 11029 11127 11225 11323 11421 11519 11617 11715
## [13849] 11813 11911 12009 12107 12205 12303 12401 12499 12597 12695 12793 12891
## [13861] 12989 13087 13185 13283 13381 13479 13577 13675 13773 13871 13969 14067
## [13873] 14165 14263 14361 14459 14557 14655 14753 14851 14949 15047 15145 15243
## [13885] 15341 15439 15537 15635 15733 15831 15929 16027 16125 16223 16321 16419
## [13897] 16517 16615 16713 16811 16909 17007 17105 17203 17301 17399 17497 17595
## [13909] 17693 17791 17889 17987 18085 18183 18281 18379 18477 18575 18673 18771
## [13921] 18869 18967 19065 19163 19261 19359 19457 19555 19653 19751 19849 19947
## [13933] 20045 20143 20241 20339 20437 20535 20633 20731 20829 20927 21025 21123
## [13945] 21221 21319 21417 21515 21613 21711 21809 21907 22005 22103 22201 22299
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## [13993] 54 152 250 348 446 544 642 740 838 936 1034 1132
## [14005] 1230 1328 1426 1524 1622 1720 1818 1916 2014 2112 2210 2308
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## [14053] 5934 6032 6130 6228 6326 6424 6522 6620 6718 6816 6914 7012
## [14065] 7110 7208 7306 7404 7502 7600 7698 7796 7894 7992 8090 8188
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## [14257] 55 153 251 349 447 545 643 741 839 937 1035 1133
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## [14293] 3583 3681 3779 3877 3975 4073 4171 4269 4367 4465 4563 4661
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## [14521] 56 154 252 350 448 546 644 742 840 938 1036 1134
## [14533] 1232 1330 1428 1526 1624 1722 1820 1918 2016 2114 2212 2310
## [14545] 2408 2506 2604 2702 2800 2898 2996 3094 3192 3290 3388 3486
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## [14581] 5936 6034 6132 6230 6328 6426 6524 6622 6720 6818 6916 7014
## [14593] 7112 7210 7308 7406 7504 7602 7700 7798 7896 7994 8092 8190
## [14605] 8288 8386 8484 8582 8680 8778 8876 8974 9072 9170 9268 9366
## [14617] 9464 9562 9660 9758 9856 9954 10052 10150 10248 10346 10444 10542
## [14629] 10640 10738 10836 10934 11032 11130 11228 11326 11424 11522 11620 11718
## [14641] 11816 11914 12012 12110 12208 12306 12404 12502 12600 12698 12796 12894
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## [14677] 15344 15442 15540 15638 15736 15834 15932 16030 16128 16226 16324 16422
## [14689] 16520 16618 16716 16814 16912 17010 17108 17206 17304 17402 17500 17598
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## [14713] 18872 18970 19068 19166 19264 19362 19460 19558 19656 19754 19852 19950
## [14725] 20048 20146 20244 20342 20440 20538 20636 20734 20832 20930 21028 21126
## [14737] 21224 21322 21420 21518 21616 21714 21812 21910 22008 22106 22204 22302
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## [14773] 24752 24850 24948 25046 25144 25242 25340 25438 25536 25634 25732 25830
## [14785] 57 155 253 351 449 547 645 743 841 939 1037 1135
## [14797] 1233 1331 1429 1527 1625 1723 1821 1919 2017 2115 2213 2311
## [14809] 2409 2507 2605 2703 2801 2899 2997 3095 3193 3291 3389 3487
## [14821] 3585 3683 3781 3879 3977 4075 4173 4271 4369 4467 4565 4663
## [14833] 4761 4859 4957 5055 5153 5251 5349 5447 5545 5643 5741 5839
## [14845] 5937 6035 6133 6231 6329 6427 6525 6623 6721 6819 6917 7015
## [14857] 7113 7211 7309 7407 7505 7603 7701 7799 7897 7995 8093 8191
## [14869] 8289 8387 8485 8583 8681 8779 8877 8975 9073 9171 9269 9367
## [14881] 9465 9563 9661 9759 9857 9955 10053 10151 10249 10347 10445 10543
## [14893] 10641 10739 10837 10935 11033 11131 11229 11327 11425 11523 11621 11719
## [14905] 11817 11915 12013 12111 12209 12307 12405 12503 12601 12699 12797 12895
## [14917] 12993 13091 13189 13287 13385 13483 13581 13679 13777 13875 13973 14071
## [14929] 14169 14267 14365 14463 14561 14659 14757 14855 14953 15051 15149 15247
## [14941] 15345 15443 15541 15639 15737 15835 15933 16031 16129 16227 16325 16423
## [14953] 16521 16619 16717 16815 16913 17011 17109 17207 17305 17403 17501 17599
## [14965] 17697 17795 17893 17991 18089 18187 18285 18383 18481 18579 18677 18775
## [14977] 18873 18971 19069 19167 19265 19363 19461 19559 19657 19755 19853 19951
## [14989] 20049 20147 20245 20343 20441 20539 20637 20735 20833 20931 21029 21127
## [15001] 21225 21323 21421 21519 21617 21715 21813 21911 22009 22107 22205 22303
## [15013] 22401 22499 22597 22695 22793 22891 22989 23087 23185 23283 23381 23479
## [15025] 23577 23675 23773 23871 23969 24067 24165 24263 24361 24459 24557 24655
## [15037] 24753 24851 24949 25047 25145 25243 25341 25439 25537 25635 25733 25831
## [15049] 58 156 254 352 450 548 646 744 842 940 1038 1136
## [15061] 1234 1332 1430 1528 1626 1724 1822 1920 2018 2116 2214 2312
## [15073] 2410 2508 2606 2704 2802 2900 2998 3096 3194 3292 3390 3488
## [15085] 3586 3684 3782 3880 3978 4076 4174 4272 4370 4468 4566 4664
## [15097] 4762 4860 4958 5056 5154 5252 5350 5448 5546 5644 5742 5840
## [15109] 5938 6036 6134 6232 6330 6428 6526 6624 6722 6820 6918 7016
## [15121] 7114 7212 7310 7408 7506 7604 7702 7800 7898 7996 8094 8192
## [15133] 8290 8388 8486 8584 8682 8780 8878 8976 9074 9172 9270 9368
## [15145] 9466 9564 9662 9760 9858 9956 10054 10152 10250 10348 10446 10544
## [15157] 10642 10740 10838 10936 11034 11132 11230 11328 11426 11524 11622 11720
## [15169] 11818 11916 12014 12112 12210 12308 12406 12504 12602 12700 12798 12896
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## [15193] 14170 14268 14366 14464 14562 14660 14758 14856 14954 15052 15150 15248
## [15205] 15346 15444 15542 15640 15738 15836 15934 16032 16130 16228 16326 16424
## [15217] 16522 16620 16718 16816 16914 17012 17110 17208 17306 17404 17502 17600
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## [15241] 18874 18972 19070 19168 19266 19364 19462 19560 19658 19756 19854 19952
## [15253] 20050 20148 20246 20344 20442 20540 20638 20736 20834 20932 21030 21128
## [15265] 21226 21324 21422 21520 21618 21716 21814 21912 22010 22108 22206 22304
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## [15289] 23578 23676 23774 23872 23970 24068 24166 24264 24362 24460 24558 24656
## [15301] 24754 24852 24950 25048 25146 25244 25342 25440 25538 25636 25734 25832
## [15313] 59 157 255 353 451 549 647 745 843 941 1039 1137
## [15325] 1235 1333 1431 1529 1627 1725 1823 1921 2019 2117 2215 2313
## [15337] 2411 2509 2607 2705 2803 2901 2999 3097 3195 3293 3391 3489
## [15349] 3587 3685 3783 3881 3979 4077 4175 4273 4371 4469 4567 4665
## [15361] 4763 4861 4959 5057 5155 5253 5351 5449 5547 5645 5743 5841
## [15373] 5939 6037 6135 6233 6331 6429 6527 6625 6723 6821 6919 7017
## [15385] 7115 7213 7311 7409 7507 7605 7703 7801 7899 7997 8095 8193
## [15397] 8291 8389 8487 8585 8683 8781 8879 8977 9075 9173 9271 9369
## [15409] 9467 9565 9663 9761 9859 9957 10055 10153 10251 10349 10447 10545
## [15421] 10643 10741 10839 10937 11035 11133 11231 11329 11427 11525 11623 11721
## [15433] 11819 11917 12015 12113 12211 12309 12407 12505 12603 12701 12799 12897
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## [15457] 14171 14269 14367 14465 14563 14661 14759 14857 14955 15053 15151 15249
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## [15481] 16523 16621 16719 16817 16915 17013 17111 17209 17307 17405 17503 17601
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## [15577] 60 158 256 354 452 550 648 746 844 942 1040 1138
## [15589] 1236 1334 1432 1530 1628 1726 1824 1922 2020 2118 2216 2314
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## [16897] 65 163 261 359 457 555 653 751 849 947 1045 1143
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## [17113] 21233 21331 21429 21527 21625 21723 21821 21919 22017 22115 22213 22311
## [17125] 22409 22507 22605 22703 22801 22899 22997 23095 23193 23291 23389 23487
## [17137] 23585 23683 23781 23879 23977 24075 24173 24271 24369 24467 24565 24663
## [17149] 24761 24859 24957 25055 25153 25251 25349 25447 25545 25643 25741 25839
## [17161] 66 164 262 360 458 556 654 752 850 948 1046 1144
## [17173] 1242 1340 1438 1536 1634 1732 1830 1928 2026 2124 2222 2320
## [17185] 2418 2516 2614 2712 2810 2908 3006 3104 3202 3300 3398 3496
## [17197] 3594 3692 3790 3888 3986 4084 4182 4280 4378 4476 4574 4672
## [17209] 4770 4868 4966 5064 5162 5260 5358 5456 5554 5652 5750 5848
## [17221] 5946 6044 6142 6240 6338 6436 6534 6632 6730 6828 6926 7024
## [17233] 7122 7220 7318 7416 7514 7612 7710 7808 7906 8004 8102 8200
## [17245] 8298 8396 8494 8592 8690 8788 8886 8984 9082 9180 9278 9376
## [17257] 9474 9572 9670 9768 9866 9964 10062 10160 10258 10356 10454 10552
## [17269] 10650 10748 10846 10944 11042 11140 11238 11336 11434 11532 11630 11728
## [17281] 11826 11924 12022 12120 12218 12316 12414 12512 12610 12708 12806 12904
## [17293] 13002 13100 13198 13296 13394 13492 13590 13688 13786 13884 13982 14080
## [17305] 14178 14276 14374 14472 14570 14668 14766 14864 14962 15060 15158 15256
## [17317] 15354 15452 15550 15648 15746 15844 15942 16040 16138 16236 16334 16432
## [17329] 16530 16628 16726 16824 16922 17020 17118 17216 17314 17412 17510 17608
## [17341] 17706 17804 17902 18000 18098 18196 18294 18392 18490 18588 18686 18784
## [17353] 18882 18980 19078 19176 19274 19372 19470 19568 19666 19764 19862 19960
## [17365] 20058 20156 20254 20352 20450 20548 20646 20744 20842 20940 21038 21136
## [17377] 21234 21332 21430 21528 21626 21724 21822 21920 22018 22116 22214 22312
## [17389] 22410 22508 22606 22704 22802 22900 22998 23096 23194 23292 23390 23488
## [17401] 23586 23684 23782 23880 23978 24076 24174 24272 24370 24468 24566 24664
## [17413] 24762 24860 24958 25056 25154 25252 25350 25448 25546 25644 25742 25840
## [17425] 67 165 263 361 459 557 655 753 851 949 1047 1145
## [17437] 1243 1341 1439 1537 1635 1733 1831 1929 2027 2125 2223 2321
## [17449] 2419 2517 2615 2713 2811 2909 3007 3105 3203 3301 3399 3497
## [17461] 3595 3693 3791 3889 3987 4085 4183 4281 4379 4477 4575 4673
## [17473] 4771 4869 4967 5065 5163 5261 5359 5457 5555 5653 5751 5849
## [17485] 5947 6045 6143 6241 6339 6437 6535 6633 6731 6829 6927 7025
## [17497] 7123 7221 7319 7417 7515 7613 7711 7809 7907 8005 8103 8201
## [17509] 8299 8397 8495 8593 8691 8789 8887 8985 9083 9181 9279 9377
## [17521] 9475 9573 9671 9769 9867 9965 10063 10161 10259 10357 10455 10553
## [17533] 10651 10749 10847 10945 11043 11141 11239 11337 11435 11533 11631 11729
## [17545] 11827 11925 12023 12121 12219 12317 12415 12513 12611 12709 12807 12905
## [17557] 13003 13101 13199 13297 13395 13493 13591 13689 13787 13885 13983 14081
## [17569] 14179 14277 14375 14473 14571 14669 14767 14865 14963 15061 15159 15257
## [17581] 15355 15453 15551 15649 15747 15845 15943 16041 16139 16237 16335 16433
## [17593] 16531 16629 16727 16825 16923 17021 17119 17217 17315 17413 17511 17609
## [17605] 17707 17805 17903 18001 18099 18197 18295 18393 18491 18589 18687 18785
## [17617] 18883 18981 19079 19177 19275 19373 19471 19569 19667 19765 19863 19961
## [17629] 20059 20157 20255 20353 20451 20549 20647 20745 20843 20941 21039 21137
## [17641] 21235 21333 21431 21529 21627 21725 21823 21921 22019 22117 22215 22313
## [17653] 22411 22509 22607 22705 22803 22901 22999 23097 23195 23293 23391 23489
## [17665] 23587 23685 23783 23881 23979 24077 24175 24273 24371 24469 24567 24665
## [17677] 24763 24861 24959 25057 25155 25253 25351 25449 25547 25645 25743 25841
## [17689] 68 166 264 362 460 558 656 754 852 950 1048 1146
## [17701] 1244 1342 1440 1538 1636 1734 1832 1930 2028 2126 2224 2322
## [17713] 2420 2518 2616 2714 2812 2910 3008 3106 3204 3302 3400 3498
## [17725] 3596 3694 3792 3890 3988 4086 4184 4282 4380 4478 4576 4674
## [17737] 4772 4870 4968 5066 5164 5262 5360 5458 5556 5654 5752 5850
## [17749] 5948 6046 6144 6242 6340 6438 6536 6634 6732 6830 6928 7026
## [17761] 7124 7222 7320 7418 7516 7614 7712 7810 7908 8006 8104 8202
## [17773] 8300 8398 8496 8594 8692 8790 8888 8986 9084 9182 9280 9378
## [17785] 9476 9574 9672 9770 9868 9966 10064 10162 10260 10358 10456 10554
## [17797] 10652 10750 10848 10946 11044 11142 11240 11338 11436 11534 11632 11730
## [17809] 11828 11926 12024 12122 12220 12318 12416 12514 12612 12710 12808 12906
## [17821] 13004 13102 13200 13298 13396 13494 13592 13690 13788 13886 13984 14082
## [17833] 14180 14278 14376 14474 14572 14670 14768 14866 14964 15062 15160 15258
## [17845] 15356 15454 15552 15650 15748 15846 15944 16042 16140 16238 16336 16434
## [17857] 16532 16630 16728 16826 16924 17022 17120 17218 17316 17414 17512 17610
## [17869] 17708 17806 17904 18002 18100 18198 18296 18394 18492 18590 18688 18786
## [17881] 18884 18982 19080 19178 19276 19374 19472 19570 19668 19766 19864 19962
## [17893] 20060 20158 20256 20354 20452 20550 20648 20746 20844 20942 21040 21138
## [17905] 21236 21334 21432 21530 21628 21726 21824 21922 22020 22118 22216 22314
## [17917] 22412 22510 22608 22706 22804 22902 23000 23098 23196 23294 23392 23490
## [17929] 23588 23686 23784 23882 23980 24078 24176 24274 24372 24470 24568 24666
## [17941] 24764 24862 24960 25058 25156 25254 25352 25450 25548 25646 25744 25842
## [17953] 69 167 265 363 461 559 657 755 853 951 1049 1147
## [17965] 1245 1343 1441 1539 1637 1735 1833 1931 2029 2127 2225 2323
## [17977] 2421 2519 2617 2715 2813 2911 3009 3107 3205 3303 3401 3499
## [17989] 3597 3695 3793 3891 3989 4087 4185 4283 4381 4479 4577 4675
## [18001] 4773 4871 4969 5067 5165 5263 5361 5459 5557 5655 5753 5851
## [18013] 5949 6047 6145 6243 6341 6439 6537 6635 6733 6831 6929 7027
## [18025] 7125 7223 7321 7419 7517 7615 7713 7811 7909 8007 8105 8203
## [18037] 8301 8399 8497 8595 8693 8791 8889 8987 9085 9183 9281 9379
## [18049] 9477 9575 9673 9771 9869 9967 10065 10163 10261 10359 10457 10555
## [18061] 10653 10751 10849 10947 11045 11143 11241 11339 11437 11535 11633 11731
## [18073] 11829 11927 12025 12123 12221 12319 12417 12515 12613 12711 12809 12907
## [18085] 13005 13103 13201 13299 13397 13495 13593 13691 13789 13887 13985 14083
## [18097] 14181 14279 14377 14475 14573 14671 14769 14867 14965 15063 15161 15259
## [18109] 15357 15455 15553 15651 15749 15847 15945 16043 16141 16239 16337 16435
## [18121] 16533 16631 16729 16827 16925 17023 17121 17219 17317 17415 17513 17611
## [18133] 17709 17807 17905 18003 18101 18199 18297 18395 18493 18591 18689 18787
## [18145] 18885 18983 19081 19179 19277 19375 19473 19571 19669 19767 19865 19963
## [18157] 20061 20159 20257 20355 20453 20551 20649 20747 20845 20943 21041 21139
## [18169] 21237 21335 21433 21531 21629 21727 21825 21923 22021 22119 22217 22315
## [18181] 22413 22511 22609 22707 22805 22903 23001 23099 23197 23295 23393 23491
## [18193] 23589 23687 23785 23883 23981 24079 24177 24275 24373 24471 24569 24667
## [18205] 24765 24863 24961 25059 25157 25255 25353 25451 25549 25647 25745 25843
## [18217] 70 168 266 364 462 560 658 756 854 952 1050 1148
## [18229] 1246 1344 1442 1540 1638 1736 1834 1932 2030 2128 2226 2324
## [18241] 2422 2520 2618 2716 2814 2912 3010 3108 3206 3304 3402 3500
## [18253] 3598 3696 3794 3892 3990 4088 4186 4284 4382 4480 4578 4676
## [18265] 4774 4872 4970 5068 5166 5264 5362 5460 5558 5656 5754 5852
## [18277] 5950 6048 6146 6244 6342 6440 6538 6636 6734 6832 6930 7028
## [18289] 7126 7224 7322 7420 7518 7616 7714 7812 7910 8008 8106 8204
## [18301] 8302 8400 8498 8596 8694 8792 8890 8988 9086 9184 9282 9380
## [18313] 9478 9576 9674 9772 9870 9968 10066 10164 10262 10360 10458 10556
## [18325] 10654 10752 10850 10948 11046 11144 11242 11340 11438 11536 11634 11732
## [18337] 11830 11928 12026 12124 12222 12320 12418 12516 12614 12712 12810 12908
## [18349] 13006 13104 13202 13300 13398 13496 13594 13692 13790 13888 13986 14084
## [18361] 14182 14280 14378 14476 14574 14672 14770 14868 14966 15064 15162 15260
## [18373] 15358 15456 15554 15652 15750 15848 15946 16044 16142 16240 16338 16436
## [18385] 16534 16632 16730 16828 16926 17024 17122 17220 17318 17416 17514 17612
## [18397] 17710 17808 17906 18004 18102 18200 18298 18396 18494 18592 18690 18788
## [18409] 18886 18984 19082 19180 19278 19376 19474 19572 19670 19768 19866 19964
## [18421] 20062 20160 20258 20356 20454 20552 20650 20748 20846 20944 21042 21140
## [18433] 21238 21336 21434 21532 21630 21728 21826 21924 22022 22120 22218 22316
## [18445] 22414 22512 22610 22708 22806 22904 23002 23100 23198 23296 23394 23492
## [18457] 23590 23688 23786 23884 23982 24080 24178 24276 24374 24472 24570 24668
## [18469] 24766 24864 24962 25060 25158 25256 25354 25452 25550 25648 25746 25844
## [18481] 71 169 267 365 463 561 659 757 855 953 1051 1149
## [18493] 1247 1345 1443 1541 1639 1737 1835 1933 2031 2129 2227 2325
## [18505] 2423 2521 2619 2717 2815 2913 3011 3109 3207 3305 3403 3501
## [18517] 3599 3697 3795 3893 3991 4089 4187 4285 4383 4481 4579 4677
## [18529] 4775 4873 4971 5069 5167 5265 5363 5461 5559 5657 5755 5853
## [18541] 5951 6049 6147 6245 6343 6441 6539 6637 6735 6833 6931 7029
## [18553] 7127 7225 7323 7421 7519 7617 7715 7813 7911 8009 8107 8205
## [18565] 8303 8401 8499 8597 8695 8793 8891 8989 9087 9185 9283 9381
## [18577] 9479 9577 9675 9773 9871 9969 10067 10165 10263 10361 10459 10557
## [18589] 10655 10753 10851 10949 11047 11145 11243 11341 11439 11537 11635 11733
## [18601] 11831 11929 12027 12125 12223 12321 12419 12517 12615 12713 12811 12909
## [18613] 13007 13105 13203 13301 13399 13497 13595 13693 13791 13889 13987 14085
## [18625] 14183 14281 14379 14477 14575 14673 14771 14869 14967 15065 15163 15261
## [18637] 15359 15457 15555 15653 15751 15849 15947 16045 16143 16241 16339 16437
## [18649] 16535 16633 16731 16829 16927 17025 17123 17221 17319 17417 17515 17613
## [18661] 17711 17809 17907 18005 18103 18201 18299 18397 18495 18593 18691 18789
## [18673] 18887 18985 19083 19181 19279 19377 19475 19573 19671 19769 19867 19965
## [18685] 20063 20161 20259 20357 20455 20553 20651 20749 20847 20945 21043 21141
## [18697] 21239 21337 21435 21533 21631 21729 21827 21925 22023 22121 22219 22317
## [18709] 22415 22513 22611 22709 22807 22905 23003 23101 23199 23297 23395 23493
## [18721] 23591 23689 23787 23885 23983 24081 24179 24277 24375 24473 24571 24669
## [18733] 24767 24865 24963 25061 25159 25257 25355 25453 25551 25649 25747 25845
## [18745] 72 170 268 366 464 562 660 758 856 954 1052 1150
## [18757] 1248 1346 1444 1542 1640 1738 1836 1934 2032 2130 2228 2326
## [18769] 2424 2522 2620 2718 2816 2914 3012 3110 3208 3306 3404 3502
## [18781] 3600 3698 3796 3894 3992 4090 4188 4286 4384 4482 4580 4678
## [18793] 4776 4874 4972 5070 5168 5266 5364 5462 5560 5658 5756 5854
## [18805] 5952 6050 6148 6246 6344 6442 6540 6638 6736 6834 6932 7030
## [18817] 7128 7226 7324 7422 7520 7618 7716 7814 7912 8010 8108 8206
## [18829] 8304 8402 8500 8598 8696 8794 8892 8990 9088 9186 9284 9382
## [18841] 9480 9578 9676 9774 9872 9970 10068 10166 10264 10362 10460 10558
## [18853] 10656 10754 10852 10950 11048 11146 11244 11342 11440 11538 11636 11734
## [18865] 11832 11930 12028 12126 12224 12322 12420 12518 12616 12714 12812 12910
## [18877] 13008 13106 13204 13302 13400 13498 13596 13694 13792 13890 13988 14086
## [18889] 14184 14282 14380 14478 14576 14674 14772 14870 14968 15066 15164 15262
## [18901] 15360 15458 15556 15654 15752 15850 15948 16046 16144 16242 16340 16438
## [18913] 16536 16634 16732 16830 16928 17026 17124 17222 17320 17418 17516 17614
## [18925] 17712 17810 17908 18006 18104 18202 18300 18398 18496 18594 18692 18790
## [18937] 18888 18986 19084 19182 19280 19378 19476 19574 19672 19770 19868 19966
## [18949] 20064 20162 20260 20358 20456 20554 20652 20750 20848 20946 21044 21142
## [18961] 21240 21338 21436 21534 21632 21730 21828 21926 22024 22122 22220 22318
## [18973] 22416 22514 22612 22710 22808 22906 23004 23102 23200 23298 23396 23494
## [18985] 23592 23690 23788 23886 23984 24082 24180 24278 24376 24474 24572 24670
## [18997] 24768 24866 24964 25062 25160 25258 25356 25454 25552 25650 25748 25846
## [19009] 73 171 269 367 465 563 661 759 857 955 1053 1151
## [19021] 1249 1347 1445 1543 1641 1739 1837 1935 2033 2131 2229 2327
## [19033] 2425 2523 2621 2719 2817 2915 3013 3111 3209 3307 3405 3503
## [19045] 3601 3699 3797 3895 3993 4091 4189 4287 4385 4483 4581 4679
## [19057] 4777 4875 4973 5071 5169 5267 5365 5463 5561 5659 5757 5855
## [19069] 5953 6051 6149 6247 6345 6443 6541 6639 6737 6835 6933 7031
## [19081] 7129 7227 7325 7423 7521 7619 7717 7815 7913 8011 8109 8207
## [19093] 8305 8403 8501 8599 8697 8795 8893 8991 9089 9187 9285 9383
## [19105] 9481 9579 9677 9775 9873 9971 10069 10167 10265 10363 10461 10559
## [19117] 10657 10755 10853 10951 11049 11147 11245 11343 11441 11539 11637 11735
## [19129] 11833 11931 12029 12127 12225 12323 12421 12519 12617 12715 12813 12911
## [19141] 13009 13107 13205 13303 13401 13499 13597 13695 13793 13891 13989 14087
## [19153] 14185 14283 14381 14479 14577 14675 14773 14871 14969 15067 15165 15263
## [19165] 15361 15459 15557 15655 15753 15851 15949 16047 16145 16243 16341 16439
## [19177] 16537 16635 16733 16831 16929 17027 17125 17223 17321 17419 17517 17615
## [19189] 17713 17811 17909 18007 18105 18203 18301 18399 18497 18595 18693 18791
## [19201] 18889 18987 19085 19183 19281 19379 19477 19575 19673 19771 19869 19967
## [19213] 20065 20163 20261 20359 20457 20555 20653 20751 20849 20947 21045 21143
## [19225] 21241 21339 21437 21535 21633 21731 21829 21927 22025 22123 22221 22319
## [19237] 22417 22515 22613 22711 22809 22907 23005 23103 23201 23299 23397 23495
## [19249] 23593 23691 23789 23887 23985 24083 24181 24279 24377 24475 24573 24671
## [19261] 24769 24867 24965 25063 25161 25259 25357 25455 25553 25651 25749 25847
## [19273] 74 172 270 368 466 564 662 760 858 956 1054 1152
## [19285] 1250 1348 1446 1544 1642 1740 1838 1936 2034 2132 2230 2328
## [19297] 2426 2524 2622 2720 2818 2916 3014 3112 3210 3308 3406 3504
## [19309] 3602 3700 3798 3896 3994 4092 4190 4288 4386 4484 4582 4680
## [19321] 4778 4876 4974 5072 5170 5268 5366 5464 5562 5660 5758 5856
## [19333] 5954 6052 6150 6248 6346 6444 6542 6640 6738 6836 6934 7032
## [19345] 7130 7228 7326 7424 7522 7620 7718 7816 7914 8012 8110 8208
## [19357] 8306 8404 8502 8600 8698 8796 8894 8992 9090 9188 9286 9384
## [19369] 9482 9580 9678 9776 9874 9972 10070 10168 10266 10364 10462 10560
## [19381] 10658 10756 10854 10952 11050 11148 11246 11344 11442 11540 11638 11736
## [19393] 11834 11932 12030 12128 12226 12324 12422 12520 12618 12716 12814 12912
## [19405] 13010 13108 13206 13304 13402 13500 13598 13696 13794 13892 13990 14088
## [19417] 14186 14284 14382 14480 14578 14676 14774 14872 14970 15068 15166 15264
## [19429] 15362 15460 15558 15656 15754 15852 15950 16048 16146 16244 16342 16440
## [19441] 16538 16636 16734 16832 16930 17028 17126 17224 17322 17420 17518 17616
## [19453] 17714 17812 17910 18008 18106 18204 18302 18400 18498 18596 18694 18792
## [19465] 18890 18988 19086 19184 19282 19380 19478 19576 19674 19772 19870 19968
## [19477] 20066 20164 20262 20360 20458 20556 20654 20752 20850 20948 21046 21144
## [19489] 21242 21340 21438 21536 21634 21732 21830 21928 22026 22124 22222 22320
## [19501] 22418 22516 22614 22712 22810 22908 23006 23104 23202 23300 23398 23496
## [19513] 23594 23692 23790 23888 23986 24084 24182 24280 24378 24476 24574 24672
## [19525] 24770 24868 24966 25064 25162 25260 25358 25456 25554 25652 25750 25848
## [19537] 75 173 271 369 467 565 663 761 859 957 1055 1153
## [19549] 1251 1349 1447 1545 1643 1741 1839 1937 2035 2133 2231 2329
## [19561] 2427 2525 2623 2721 2819 2917 3015 3113 3211 3309 3407 3505
## [19573] 3603 3701 3799 3897 3995 4093 4191 4289 4387 4485 4583 4681
## [19585] 4779 4877 4975 5073 5171 5269 5367 5465 5563 5661 5759 5857
## [19597] 5955 6053 6151 6249 6347 6445 6543 6641 6739 6837 6935 7033
## [19609] 7131 7229 7327 7425 7523 7621 7719 7817 7915 8013 8111 8209
## [19621] 8307 8405 8503 8601 8699 8797 8895 8993 9091 9189 9287 9385
## [19633] 9483 9581 9679 9777 9875 9973 10071 10169 10267 10365 10463 10561
## [19645] 10659 10757 10855 10953 11051 11149 11247 11345 11443 11541 11639 11737
## [19657] 11835 11933 12031 12129 12227 12325 12423 12521 12619 12717 12815 12913
## [19669] 13011 13109 13207 13305 13403 13501 13599 13697 13795 13893 13991 14089
## [19681] 14187 14285 14383 14481 14579 14677 14775 14873 14971 15069 15167 15265
## [19693] 15363 15461 15559 15657 15755 15853 15951 16049 16147 16245 16343 16441
## [19705] 16539 16637 16735 16833 16931 17029 17127 17225 17323 17421 17519 17617
## [19717] 17715 17813 17911 18009 18107 18205 18303 18401 18499 18597 18695 18793
## [19729] 18891 18989 19087 19185 19283 19381 19479 19577 19675 19773 19871 19969
## [19741] 20067 20165 20263 20361 20459 20557 20655 20753 20851 20949 21047 21145
## [19753] 21243 21341 21439 21537 21635 21733 21831 21929 22027 22125 22223 22321
## [19765] 22419 22517 22615 22713 22811 22909 23007 23105 23203 23301 23399 23497
## [19777] 23595 23693 23791 23889 23987 24085 24183 24281 24379 24477 24575 24673
## [19789] 24771 24869 24967 25065 25163 25261 25359 25457 25555 25653 25751 25849
## [19801] 76 174 272 370 468 566 664 762 860 958 1056 1154
## [19813] 1252 1350 1448 1546 1644 1742 1840 1938 2036 2134 2232 2330
## [19825] 2428 2526 2624 2722 2820 2918 3016 3114 3212 3310 3408 3506
## [19837] 3604 3702 3800 3898 3996 4094 4192 4290 4388 4486 4584 4682
## [19849] 4780 4878 4976 5074 5172 5270 5368 5466 5564 5662 5760 5858
## [19861] 5956 6054 6152 6250 6348 6446 6544 6642 6740 6838 6936 7034
## [19873] 7132 7230 7328 7426 7524 7622 7720 7818 7916 8014 8112 8210
## [19885] 8308 8406 8504 8602 8700 8798 8896 8994 9092 9190 9288 9386
## [19897] 9484 9582 9680 9778 9876 9974 10072 10170 10268 10366 10464 10562
## [19909] 10660 10758 10856 10954 11052 11150 11248 11346 11444 11542 11640 11738
## [19921] 11836 11934 12032 12130 12228 12326 12424 12522 12620 12718 12816 12914
## [19933] 13012 13110 13208 13306 13404 13502 13600 13698 13796 13894 13992 14090
## [19945] 14188 14286 14384 14482 14580 14678 14776 14874 14972 15070 15168 15266
## [19957] 15364 15462 15560 15658 15756 15854 15952 16050 16148 16246 16344 16442
## [19969] 16540 16638 16736 16834 16932 17030 17128 17226 17324 17422 17520 17618
## [19981] 17716 17814 17912 18010 18108 18206 18304 18402 18500 18598 18696 18794
## [19993] 18892 18990 19088 19186 19284 19382 19480 19578 19676 19774 19872 19970
## [20005] 20068 20166 20264 20362 20460 20558 20656 20754 20852 20950 21048 21146
## [20017] 21244 21342 21440 21538 21636 21734 21832 21930 22028 22126 22224 22322
## [20029] 22420 22518 22616 22714 22812 22910 23008 23106 23204 23302 23400 23498
## [20041] 23596 23694 23792 23890 23988 24086 24184 24282 24380 24478 24576 24674
## [20053] 24772 24870 24968 25066 25164 25262 25360 25458 25556 25654 25752 25850
## [20065] 77 175 273 371 469 567 665 763 861 959 1057 1155
## [20077] 1253 1351 1449 1547 1645 1743 1841 1939 2037 2135 2233 2331
## [20089] 2429 2527 2625 2723 2821 2919 3017 3115 3213 3311 3409 3507
## [20101] 3605 3703 3801 3899 3997 4095 4193 4291 4389 4487 4585 4683
## [20113] 4781 4879 4977 5075 5173 5271 5369 5467 5565 5663 5761 5859
## [20125] 5957 6055 6153 6251 6349 6447 6545 6643 6741 6839 6937 7035
## [20137] 7133 7231 7329 7427 7525 7623 7721 7819 7917 8015 8113 8211
## [20149] 8309 8407 8505 8603 8701 8799 8897 8995 9093 9191 9289 9387
## [20161] 9485 9583 9681 9779 9877 9975 10073 10171 10269 10367 10465 10563
## [20173] 10661 10759 10857 10955 11053 11151 11249 11347 11445 11543 11641 11739
## [20185] 11837 11935 12033 12131 12229 12327 12425 12523 12621 12719 12817 12915
## [20197] 13013 13111 13209 13307 13405 13503 13601 13699 13797 13895 13993 14091
## [20209] 14189 14287 14385 14483 14581 14679 14777 14875 14973 15071 15169 15267
## [20221] 15365 15463 15561 15659 15757 15855 15953 16051 16149 16247 16345 16443
## [20233] 16541 16639 16737 16835 16933 17031 17129 17227 17325 17423 17521 17619
## [20245] 17717 17815 17913 18011 18109 18207 18305 18403 18501 18599 18697 18795
## [20257] 18893 18991 19089 19187 19285 19383 19481 19579 19677 19775 19873 19971
## [20269] 20069 20167 20265 20363 20461 20559 20657 20755 20853 20951 21049 21147
## [20281] 21245 21343 21441 21539 21637 21735 21833 21931 22029 22127 22225 22323
## [20293] 22421 22519 22617 22715 22813 22911 23009 23107 23205 23303 23401 23499
## [20305] 23597 23695 23793 23891 23989 24087 24185 24283 24381 24479 24577 24675
## [20317] 24773 24871 24969 25067 25165 25263 25361 25459 25557 25655 25753 25851
## [20329] 78 176 274 372 470 568 666 764 862 960 1058 1156
## [20341] 1254 1352 1450 1548 1646 1744 1842 1940 2038 2136 2234 2332
## [20353] 2430 2528 2626 2724 2822 2920 3018 3116 3214 3312 3410 3508
## [20365] 3606 3704 3802 3900 3998 4096 4194 4292 4390 4488 4586 4684
## [20377] 4782 4880 4978 5076 5174 5272 5370 5468 5566 5664 5762 5860
## [20389] 5958 6056 6154 6252 6350 6448 6546 6644 6742 6840 6938 7036
## [20401] 7134 7232 7330 7428 7526 7624 7722 7820 7918 8016 8114 8212
## [20413] 8310 8408 8506 8604 8702 8800 8898 8996 9094 9192 9290 9388
## [20425] 9486 9584 9682 9780 9878 9976 10074 10172 10270 10368 10466 10564
## [20437] 10662 10760 10858 10956 11054 11152 11250 11348 11446 11544 11642 11740
## [20449] 11838 11936 12034 12132 12230 12328 12426 12524 12622 12720 12818 12916
## [20461] 13014 13112 13210 13308 13406 13504 13602 13700 13798 13896 13994 14092
## [20473] 14190 14288 14386 14484 14582 14680 14778 14876 14974 15072 15170 15268
## [20485] 15366 15464 15562 15660 15758 15856 15954 16052 16150 16248 16346 16444
## [20497] 16542 16640 16738 16836 16934 17032 17130 17228 17326 17424 17522 17620
## [20509] 17718 17816 17914 18012 18110 18208 18306 18404 18502 18600 18698 18796
## [20521] 18894 18992 19090 19188 19286 19384 19482 19580 19678 19776 19874 19972
## [20533] 20070 20168 20266 20364 20462 20560 20658 20756 20854 20952 21050 21148
## [20545] 21246 21344 21442 21540 21638 21736 21834 21932 22030 22128 22226 22324
## [20557] 22422 22520 22618 22716 22814 22912 23010 23108 23206 23304 23402 23500
## [20569] 23598 23696 23794 23892 23990 24088 24186 24284 24382 24480 24578 24676
## [20581] 24774 24872 24970 25068 25166 25264 25362 25460 25558 25656 25754 25852
## [20593] 79 177 275 373 471 569 667 765 863 961 1059 1157
## [20605] 1255 1353 1451 1549 1647 1745 1843 1941 2039 2137 2235 2333
## [20617] 2431 2529 2627 2725 2823 2921 3019 3117 3215 3313 3411 3509
## [20629] 3607 3705 3803 3901 3999 4097 4195 4293 4391 4489 4587 4685
## [20641] 4783 4881 4979 5077 5175 5273 5371 5469 5567 5665 5763 5861
## [20653] 5959 6057 6155 6253 6351 6449 6547 6645 6743 6841 6939 7037
## [20665] 7135 7233 7331 7429 7527 7625 7723 7821 7919 8017 8115 8213
## [20677] 8311 8409 8507 8605 8703 8801 8899 8997 9095 9193 9291 9389
## [20689] 9487 9585 9683 9781 9879 9977 10075 10173 10271 10369 10467 10565
## [20701] 10663 10761 10859 10957 11055 11153 11251 11349 11447 11545 11643 11741
## [20713] 11839 11937 12035 12133 12231 12329 12427 12525 12623 12721 12819 12917
## [20725] 13015 13113 13211 13309 13407 13505 13603 13701 13799 13897 13995 14093
## [20737] 14191 14289 14387 14485 14583 14681 14779 14877 14975 15073 15171 15269
## [20749] 15367 15465 15563 15661 15759 15857 15955 16053 16151 16249 16347 16445
## [20761] 16543 16641 16739 16837 16935 17033 17131 17229 17327 17425 17523 17621
## [20773] 17719 17817 17915 18013 18111 18209 18307 18405 18503 18601 18699 18797
## [20785] 18895 18993 19091 19189 19287 19385 19483 19581 19679 19777 19875 19973
## [20797] 20071 20169 20267 20365 20463 20561 20659 20757 20855 20953 21051 21149
## [20809] 21247 21345 21443 21541 21639 21737 21835 21933 22031 22129 22227 22325
## [20821] 22423 22521 22619 22717 22815 22913 23011 23109 23207 23305 23403 23501
## [20833] 23599 23697 23795 23893 23991 24089 24187 24285 24383 24481 24579 24677
## [20845] 24775 24873 24971 25069 25167 25265 25363 25461 25559 25657 25755 25853
## [20857] 80 178 276 374 472 570 668 766 864 962 1060 1158
## [20869] 1256 1354 1452 1550 1648 1746 1844 1942 2040 2138 2236 2334
## [20881] 2432 2530 2628 2726 2824 2922 3020 3118 3216 3314 3412 3510
## [20893] 3608 3706 3804 3902 4000 4098 4196 4294 4392 4490 4588 4686
## [20905] 4784 4882 4980 5078 5176 5274 5372 5470 5568 5666 5764 5862
## [20917] 5960 6058 6156 6254 6352 6450 6548 6646 6744 6842 6940 7038
## [20929] 7136 7234 7332 7430 7528 7626 7724 7822 7920 8018 8116 8214
## [20941] 8312 8410 8508 8606 8704 8802 8900 8998 9096 9194 9292 9390
## [20953] 9488 9586 9684 9782 9880 9978 10076 10174 10272 10370 10468 10566
## [20965] 10664 10762 10860 10958 11056 11154 11252 11350 11448 11546 11644 11742
## [20977] 11840 11938 12036 12134 12232 12330 12428 12526 12624 12722 12820 12918
## [20989] 13016 13114 13212 13310 13408 13506 13604 13702 13800 13898 13996 14094
## [21001] 14192 14290 14388 14486 14584 14682 14780 14878 14976 15074 15172 15270
## [21013] 15368 15466 15564 15662 15760 15858 15956 16054 16152 16250 16348 16446
## [21025] 16544 16642 16740 16838 16936 17034 17132 17230 17328 17426 17524 17622
## [21037] 17720 17818 17916 18014 18112 18210 18308 18406 18504 18602 18700 18798
## [21049] 18896 18994 19092 19190 19288 19386 19484 19582 19680 19778 19876 19974
## [21061] 20072 20170 20268 20366 20464 20562 20660 20758 20856 20954 21052 21150
## [21073] 21248 21346 21444 21542 21640 21738 21836 21934 22032 22130 22228 22326
## [21085] 22424 22522 22620 22718 22816 22914 23012 23110 23208 23306 23404 23502
## [21097] 23600 23698 23796 23894 23992 24090 24188 24286 24384 24482 24580 24678
## [21109] 24776 24874 24972 25070 25168 25266 25364 25462 25560 25658 25756 25854
## [21121] 81 179 277 375 473 571 669 767 865 963 1061 1159
## [21133] 1257 1355 1453 1551 1649 1747 1845 1943 2041 2139 2237 2335
## [21145] 2433 2531 2629 2727 2825 2923 3021 3119 3217 3315 3413 3511
## [21157] 3609 3707 3805 3903 4001 4099 4197 4295 4393 4491 4589 4687
## [21169] 4785 4883 4981 5079 5177 5275 5373 5471 5569 5667 5765 5863
## [21181] 5961 6059 6157 6255 6353 6451 6549 6647 6745 6843 6941 7039
## [21193] 7137 7235 7333 7431 7529 7627 7725 7823 7921 8019 8117 8215
## [21205] 8313 8411 8509 8607 8705 8803 8901 8999 9097 9195 9293 9391
## [21217] 9489 9587 9685 9783 9881 9979 10077 10175 10273 10371 10469 10567
## [21229] 10665 10763 10861 10959 11057 11155 11253 11351 11449 11547 11645 11743
## [21241] 11841 11939 12037 12135 12233 12331 12429 12527 12625 12723 12821 12919
## [21253] 13017 13115 13213 13311 13409 13507 13605 13703 13801 13899 13997 14095
## [21265] 14193 14291 14389 14487 14585 14683 14781 14879 14977 15075 15173 15271
## [21277] 15369 15467 15565 15663 15761 15859 15957 16055 16153 16251 16349 16447
## [21289] 16545 16643 16741 16839 16937 17035 17133 17231 17329 17427 17525 17623
## [21301] 17721 17819 17917 18015 18113 18211 18309 18407 18505 18603 18701 18799
## [21313] 18897 18995 19093 19191 19289 19387 19485 19583 19681 19779 19877 19975
## [21325] 20073 20171 20269 20367 20465 20563 20661 20759 20857 20955 21053 21151
## [21337] 21249 21347 21445 21543 21641 21739 21837 21935 22033 22131 22229 22327
## [21349] 22425 22523 22621 22719 22817 22915 23013 23111 23209 23307 23405 23503
## [21361] 23601 23699 23797 23895 23993 24091 24189 24287 24385 24483 24581 24679
## [21373] 24777 24875 24973 25071 25169 25267 25365 25463 25561 25659 25757 25855
## [21385] 82 180 278 376 474 572 670 768 866 964 1062 1160
## [21397] 1258 1356 1454 1552 1650 1748 1846 1944 2042 2140 2238 2336
## [21409] 2434 2532 2630 2728 2826 2924 3022 3120 3218 3316 3414 3512
## [21421] 3610 3708 3806 3904 4002 4100 4198 4296 4394 4492 4590 4688
## [21433] 4786 4884 4982 5080 5178 5276 5374 5472 5570 5668 5766 5864
## [21445] 5962 6060 6158 6256 6354 6452 6550 6648 6746 6844 6942 7040
## [21457] 7138 7236 7334 7432 7530 7628 7726 7824 7922 8020 8118 8216
## [21469] 8314 8412 8510 8608 8706 8804 8902 9000 9098 9196 9294 9392
## [21481] 9490 9588 9686 9784 9882 9980 10078 10176 10274 10372 10470 10568
## [21493] 10666 10764 10862 10960 11058 11156 11254 11352 11450 11548 11646 11744
## [21505] 11842 11940 12038 12136 12234 12332 12430 12528 12626 12724 12822 12920
## [21517] 13018 13116 13214 13312 13410 13508 13606 13704 13802 13900 13998 14096
## [21529] 14194 14292 14390 14488 14586 14684 14782 14880 14978 15076 15174 15272
## [21541] 15370 15468 15566 15664 15762 15860 15958 16056 16154 16252 16350 16448
## [21553] 16546 16644 16742 16840 16938 17036 17134 17232 17330 17428 17526 17624
## [21565] 17722 17820 17918 18016 18114 18212 18310 18408 18506 18604 18702 18800
## [21577] 18898 18996 19094 19192 19290 19388 19486 19584 19682 19780 19878 19976
## [21589] 20074 20172 20270 20368 20466 20564 20662 20760 20858 20956 21054 21152
## [21601] 21250 21348 21446 21544 21642 21740 21838 21936 22034 22132 22230 22328
## [21613] 22426 22524 22622 22720 22818 22916 23014 23112 23210 23308 23406 23504
## [21625] 23602 23700 23798 23896 23994 24092 24190 24288 24386 24484 24582 24680
## [21637] 24778 24876 24974 25072 25170 25268 25366 25464 25562 25660 25758 25856
## [21649] 83 181 279 377 475 573 671 769 867 965 1063 1161
## [21661] 1259 1357 1455 1553 1651 1749 1847 1945 2043 2141 2239 2337
## [21673] 2435 2533 2631 2729 2827 2925 3023 3121 3219 3317 3415 3513
## [21685] 3611 3709 3807 3905 4003 4101 4199 4297 4395 4493 4591 4689
## [21697] 4787 4885 4983 5081 5179 5277 5375 5473 5571 5669 5767 5865
## [21709] 5963 6061 6159 6257 6355 6453 6551 6649 6747 6845 6943 7041
## [21721] 7139 7237 7335 7433 7531 7629 7727 7825 7923 8021 8119 8217
## [21733] 8315 8413 8511 8609 8707 8805 8903 9001 9099 9197 9295 9393
## [21745] 9491 9589 9687 9785 9883 9981 10079 10177 10275 10373 10471 10569
## [21757] 10667 10765 10863 10961 11059 11157 11255 11353 11451 11549 11647 11745
## [21769] 11843 11941 12039 12137 12235 12333 12431 12529 12627 12725 12823 12921
## [21781] 13019 13117 13215 13313 13411 13509 13607 13705 13803 13901 13999 14097
## [21793] 14195 14293 14391 14489 14587 14685 14783 14881 14979 15077 15175 15273
## [21805] 15371 15469 15567 15665 15763 15861 15959 16057 16155 16253 16351 16449
## [21817] 16547 16645 16743 16841 16939 17037 17135 17233 17331 17429 17527 17625
## [21829] 17723 17821 17919 18017 18115 18213 18311 18409 18507 18605 18703 18801
## [21841] 18899 18997 19095 19193 19291 19389 19487 19585 19683 19781 19879 19977
## [21853] 20075 20173 20271 20369 20467 20565 20663 20761 20859 20957 21055 21153
## [21865] 21251 21349 21447 21545 21643 21741 21839 21937 22035 22133 22231 22329
## [21877] 22427 22525 22623 22721 22819 22917 23015 23113 23211 23309 23407 23505
## [21889] 23603 23701 23799 23897 23995 24093 24191 24289 24387 24485 24583 24681
## [21901] 24779 24877 24975 25073 25171 25269 25367 25465 25563 25661 25759 25857
## [21913] 84 182 280 378 476 574 672 770 868 966 1064 1162
## [21925] 1260 1358 1456 1554 1652 1750 1848 1946 2044 2142 2240 2338
## [21937] 2436 2534 2632 2730 2828 2926 3024 3122 3220 3318 3416 3514
## [21949] 3612 3710 3808 3906 4004 4102 4200 4298 4396 4494 4592 4690
## [21961] 4788 4886 4984 5082 5180 5278 5376 5474 5572 5670 5768 5866
## [21973] 5964 6062 6160 6258 6356 6454 6552 6650 6748 6846 6944 7042
## [21985] 7140 7238 7336 7434 7532 7630 7728 7826 7924 8022 8120 8218
## [21997] 8316 8414 8512 8610 8708 8806 8904 9002 9100 9198 9296 9394
## [22009] 9492 9590 9688 9786 9884 9982 10080 10178 10276 10374 10472 10570
## [22021] 10668 10766 10864 10962 11060 11158 11256 11354 11452 11550 11648 11746
## [22033] 11844 11942 12040 12138 12236 12334 12432 12530 12628 12726 12824 12922
## [22045] 13020 13118 13216 13314 13412 13510 13608 13706 13804 13902 14000 14098
## [22057] 14196 14294 14392 14490 14588 14686 14784 14882 14980 15078 15176 15274
## [22069] 15372 15470 15568 15666 15764 15862 15960 16058 16156 16254 16352 16450
## [22081] 16548 16646 16744 16842 16940 17038 17136 17234 17332 17430 17528 17626
## [22093] 17724 17822 17920 18018 18116 18214 18312 18410 18508 18606 18704 18802
## [22105] 18900 18998 19096 19194 19292 19390 19488 19586 19684 19782 19880 19978
## [22117] 20076 20174 20272 20370 20468 20566 20664 20762 20860 20958 21056 21154
## [22129] 21252 21350 21448 21546 21644 21742 21840 21938 22036 22134 22232 22330
## [22141] 22428 22526 22624 22722 22820 22918 23016 23114 23212 23310 23408 23506
## [22153] 23604 23702 23800 23898 23996 24094 24192 24290 24388 24486 24584 24682
## [22165] 24780 24878 24976 25074 25172 25270 25368 25466 25564 25662 25760 25858
## [22177] 85 183 281 379 477 575 673 771 869 967 1065 1163
## [22189] 1261 1359 1457 1555 1653 1751 1849 1947 2045 2143 2241 2339
## [22201] 2437 2535 2633 2731 2829 2927 3025 3123 3221 3319 3417 3515
## [22213] 3613 3711 3809 3907 4005 4103 4201 4299 4397 4495 4593 4691
## [22225] 4789 4887 4985 5083 5181 5279 5377 5475 5573 5671 5769 5867
## [22237] 5965 6063 6161 6259 6357 6455 6553 6651 6749 6847 6945 7043
## [22249] 7141 7239 7337 7435 7533 7631 7729 7827 7925 8023 8121 8219
## [22261] 8317 8415 8513 8611 8709 8807 8905 9003 9101 9199 9297 9395
## [22273] 9493 9591 9689 9787 9885 9983 10081 10179 10277 10375 10473 10571
## [22285] 10669 10767 10865 10963 11061 11159 11257 11355 11453 11551 11649 11747
## [22297] 11845 11943 12041 12139 12237 12335 12433 12531 12629 12727 12825 12923
## [22309] 13021 13119 13217 13315 13413 13511 13609 13707 13805 13903 14001 14099
## [22321] 14197 14295 14393 14491 14589 14687 14785 14883 14981 15079 15177 15275
## [22333] 15373 15471 15569 15667 15765 15863 15961 16059 16157 16255 16353 16451
## [22345] 16549 16647 16745 16843 16941 17039 17137 17235 17333 17431 17529 17627
## [22357] 17725 17823 17921 18019 18117 18215 18313 18411 18509 18607 18705 18803
## [22369] 18901 18999 19097 19195 19293 19391 19489 19587 19685 19783 19881 19979
## [22381] 20077 20175 20273 20371 20469 20567 20665 20763 20861 20959 21057 21155
## [22393] 21253 21351 21449 21547 21645 21743 21841 21939 22037 22135 22233 22331
## [22405] 22429 22527 22625 22723 22821 22919 23017 23115 23213 23311 23409 23507
## [22417] 23605 23703 23801 23899 23997 24095 24193 24291 24389 24487 24585 24683
## [22429] 24781 24879 24977 25075 25173 25271 25369 25467 25565 25663 25761 25859
## [22441] 86 184 282 380 478 576 674 772 870 968 1066 1164
## [22453] 1262 1360 1458 1556 1654 1752 1850 1948 2046 2144 2242 2340
## [22465] 2438 2536 2634 2732 2830 2928 3026 3124 3222 3320 3418 3516
## [22477] 3614 3712 3810 3908 4006 4104 4202 4300 4398 4496 4594 4692
## [22489] 4790 4888 4986 5084 5182 5280 5378 5476 5574 5672 5770 5868
## [22501] 5966 6064 6162 6260 6358 6456 6554 6652 6750 6848 6946 7044
## [22513] 7142 7240 7338 7436 7534 7632 7730 7828 7926 8024 8122 8220
## [22525] 8318 8416 8514 8612 8710 8808 8906 9004 9102 9200 9298 9396
## [22537] 9494 9592 9690 9788 9886 9984 10082 10180 10278 10376 10474 10572
## [22549] 10670 10768 10866 10964 11062 11160 11258 11356 11454 11552 11650 11748
## [22561] 11846 11944 12042 12140 12238 12336 12434 12532 12630 12728 12826 12924
## [22573] 13022 13120 13218 13316 13414 13512 13610 13708 13806 13904 14002 14100
## [22585] 14198 14296 14394 14492 14590 14688 14786 14884 14982 15080 15178 15276
## [22597] 15374 15472 15570 15668 15766 15864 15962 16060 16158 16256 16354 16452
## [22609] 16550 16648 16746 16844 16942 17040 17138 17236 17334 17432 17530 17628
## [22621] 17726 17824 17922 18020 18118 18216 18314 18412 18510 18608 18706 18804
## [22633] 18902 19000 19098 19196 19294 19392 19490 19588 19686 19784 19882 19980
## [22645] 20078 20176 20274 20372 20470 20568 20666 20764 20862 20960 21058 21156
## [22657] 21254 21352 21450 21548 21646 21744 21842 21940 22038 22136 22234 22332
## [22669] 22430 22528 22626 22724 22822 22920 23018 23116 23214 23312 23410 23508
## [22681] 23606 23704 23802 23900 23998 24096 24194 24292 24390 24488 24586 24684
## [22693] 24782 24880 24978 25076 25174 25272 25370 25468 25566 25664 25762 25860
## [22705] 87 185 283 381 479 577 675 773 871 969 1067 1165
## [22717] 1263 1361 1459 1557 1655 1753 1851 1949 2047 2145 2243 2341
## [22729] 2439 2537 2635 2733 2831 2929 3027 3125 3223 3321 3419 3517
## [22741] 3615 3713 3811 3909 4007 4105 4203 4301 4399 4497 4595 4693
## [22753] 4791 4889 4987 5085 5183 5281 5379 5477 5575 5673 5771 5869
## [22765] 5967 6065 6163 6261 6359 6457 6555 6653 6751 6849 6947 7045
## [22777] 7143 7241 7339 7437 7535 7633 7731 7829 7927 8025 8123 8221
## [22789] 8319 8417 8515 8613 8711 8809 8907 9005 9103 9201 9299 9397
## [22801] 9495 9593 9691 9789 9887 9985 10083 10181 10279 10377 10475 10573
## [22813] 10671 10769 10867 10965 11063 11161 11259 11357 11455 11553 11651 11749
## [22825] 11847 11945 12043 12141 12239 12337 12435 12533 12631 12729 12827 12925
## [22837] 13023 13121 13219 13317 13415 13513 13611 13709 13807 13905 14003 14101
## [22849] 14199 14297 14395 14493 14591 14689 14787 14885 14983 15081 15179 15277
## [22861] 15375 15473 15571 15669 15767 15865 15963 16061 16159 16257 16355 16453
## [22873] 16551 16649 16747 16845 16943 17041 17139 17237 17335 17433 17531 17629
## [22885] 17727 17825 17923 18021 18119 18217 18315 18413 18511 18609 18707 18805
## [22897] 18903 19001 19099 19197 19295 19393 19491 19589 19687 19785 19883 19981
## [22909] 20079 20177 20275 20373 20471 20569 20667 20765 20863 20961 21059 21157
## [22921] 21255 21353 21451 21549 21647 21745 21843 21941 22039 22137 22235 22333
## [22933] 22431 22529 22627 22725 22823 22921 23019 23117 23215 23313 23411 23509
## [22945] 23607 23705 23803 23901 23999 24097 24195 24293 24391 24489 24587 24685
## [22957] 24783 24881 24979 25077 25175 25273 25371 25469 25567 25665 25763 25861
## [22969] 88 186 284 382 480 578 676 774 872 970 1068 1166
## [22981] 1264 1362 1460 1558 1656 1754 1852 1950 2048 2146 2244 2342
## [22993] 2440 2538 2636 2734 2832 2930 3028 3126 3224 3322 3420 3518
## [23005] 3616 3714 3812 3910 4008 4106 4204 4302 4400 4498 4596 4694
## [23017] 4792 4890 4988 5086 5184 5282 5380 5478 5576 5674 5772 5870
## [23029] 5968 6066 6164 6262 6360 6458 6556 6654 6752 6850 6948 7046
## [23041] 7144 7242 7340 7438 7536 7634 7732 7830 7928 8026 8124 8222
## [23053] 8320 8418 8516 8614 8712 8810 8908 9006 9104 9202 9300 9398
## [23065] 9496 9594 9692 9790 9888 9986 10084 10182 10280 10378 10476 10574
## [23077] 10672 10770 10868 10966 11064 11162 11260 11358 11456 11554 11652 11750
## [23089] 11848 11946 12044 12142 12240 12338 12436 12534 12632 12730 12828 12926
## [23101] 13024 13122 13220 13318 13416 13514 13612 13710 13808 13906 14004 14102
## [23113] 14200 14298 14396 14494 14592 14690 14788 14886 14984 15082 15180 15278
## [23125] 15376 15474 15572 15670 15768 15866 15964 16062 16160 16258 16356 16454
## [23137] 16552 16650 16748 16846 16944 17042 17140 17238 17336 17434 17532 17630
## [23149] 17728 17826 17924 18022 18120 18218 18316 18414 18512 18610 18708 18806
## [23161] 18904 19002 19100 19198 19296 19394 19492 19590 19688 19786 19884 19982
## [23173] 20080 20178 20276 20374 20472 20570 20668 20766 20864 20962 21060 21158
## [23185] 21256 21354 21452 21550 21648 21746 21844 21942 22040 22138 22236 22334
## [23197] 22432 22530 22628 22726 22824 22922 23020 23118 23216 23314 23412 23510
## [23209] 23608 23706 23804 23902 24000 24098 24196 24294 24392 24490 24588 24686
## [23221] 24784 24882 24980 25078 25176 25274 25372 25470 25568 25666 25764 25862
## [23233] 89 187 285 383 481 579 677 775 873 971 1069 1167
## [23245] 1265 1363 1461 1559 1657 1755 1853 1951 2049 2147 2245 2343
## [23257] 2441 2539 2637 2735 2833 2931 3029 3127 3225 3323 3421 3519
## [23269] 3617 3715 3813 3911 4009 4107 4205 4303 4401 4499 4597 4695
## [23281] 4793 4891 4989 5087 5185 5283 5381 5479 5577 5675 5773 5871
## [23293] 5969 6067 6165 6263 6361 6459 6557 6655 6753 6851 6949 7047
## [23305] 7145 7243 7341 7439 7537 7635 7733 7831 7929 8027 8125 8223
## [23317] 8321 8419 8517 8615 8713 8811 8909 9007 9105 9203 9301 9399
## [23329] 9497 9595 9693 9791 9889 9987 10085 10183 10281 10379 10477 10575
## [23341] 10673 10771 10869 10967 11065 11163 11261 11359 11457 11555 11653 11751
## [23353] 11849 11947 12045 12143 12241 12339 12437 12535 12633 12731 12829 12927
## [23365] 13025 13123 13221 13319 13417 13515 13613 13711 13809 13907 14005 14103
## [23377] 14201 14299 14397 14495 14593 14691 14789 14887 14985 15083 15181 15279
## [23389] 15377 15475 15573 15671 15769 15867 15965 16063 16161 16259 16357 16455
## [23401] 16553 16651 16749 16847 16945 17043 17141 17239 17337 17435 17533 17631
## [23413] 17729 17827 17925 18023 18121 18219 18317 18415 18513 18611 18709 18807
## [23425] 18905 19003 19101 19199 19297 19395 19493 19591 19689 19787 19885 19983
## [23437] 20081 20179 20277 20375 20473 20571 20669 20767 20865 20963 21061 21159
## [23449] 21257 21355 21453 21551 21649 21747 21845 21943 22041 22139 22237 22335
## [23461] 22433 22531 22629 22727 22825 22923 23021 23119 23217 23315 23413 23511
## [23473] 23609 23707 23805 23903 24001 24099 24197 24295 24393 24491 24589 24687
## [23485] 24785 24883 24981 25079 25177 25275 25373 25471 25569 25667 25765 25863
## [23497] 90 188 286 384 482 580 678 776 874 972 1070 1168
## [23509] 1266 1364 1462 1560 1658 1756 1854 1952 2050 2148 2246 2344
## [23521] 2442 2540 2638 2736 2834 2932 3030 3128 3226 3324 3422 3520
## [23533] 3618 3716 3814 3912 4010 4108 4206 4304 4402 4500 4598 4696
## [23545] 4794 4892 4990 5088 5186 5284 5382 5480 5578 5676 5774 5872
## [23557] 5970 6068 6166 6264 6362 6460 6558 6656 6754 6852 6950 7048
## [23569] 7146 7244 7342 7440 7538 7636 7734 7832 7930 8028 8126 8224
## [23581] 8322 8420 8518 8616 8714 8812 8910 9008 9106 9204 9302 9400
## [23593] 9498 9596 9694 9792 9890 9988 10086 10184 10282 10380 10478 10576
## [23605] 10674 10772 10870 10968 11066 11164 11262 11360 11458 11556 11654 11752
## [23617] 11850 11948 12046 12144 12242 12340 12438 12536 12634 12732 12830 12928
## [23629] 13026 13124 13222 13320 13418 13516 13614 13712 13810 13908 14006 14104
## [23641] 14202 14300 14398 14496 14594 14692 14790 14888 14986 15084 15182 15280
## [23653] 15378 15476 15574 15672 15770 15868 15966 16064 16162 16260 16358 16456
## [23665] 16554 16652 16750 16848 16946 17044 17142 17240 17338 17436 17534 17632
## [23677] 17730 17828 17926 18024 18122 18220 18318 18416 18514 18612 18710 18808
## [23689] 18906 19004 19102 19200 19298 19396 19494 19592 19690 19788 19886 19984
## [23701] 20082 20180 20278 20376 20474 20572 20670 20768 20866 20964 21062 21160
## [23713] 21258 21356 21454 21552 21650 21748 21846 21944 22042 22140 22238 22336
## [23725] 22434 22532 22630 22728 22826 22924 23022 23120 23218 23316 23414 23512
## [23737] 23610 23708 23806 23904 24002 24100 24198 24296 24394 24492 24590 24688
## [23749] 24786 24884 24982 25080 25178 25276 25374 25472 25570 25668 25766 25864
## [23761] 91 189 287 385 483 581 679 777 875 973 1071 1169
## [23773] 1267 1365 1463 1561 1659 1757 1855 1953 2051 2149 2247 2345
## [23785] 2443 2541 2639 2737 2835 2933 3031 3129 3227 3325 3423 3521
## [23797] 3619 3717 3815 3913 4011 4109 4207 4305 4403 4501 4599 4697
## [23809] 4795 4893 4991 5089 5187 5285 5383 5481 5579 5677 5775 5873
## [23821] 5971 6069 6167 6265 6363 6461 6559 6657 6755 6853 6951 7049
## [23833] 7147 7245 7343 7441 7539 7637 7735 7833 7931 8029 8127 8225
## [23845] 8323 8421 8519 8617 8715 8813 8911 9009 9107 9205 9303 9401
## [23857] 9499 9597 9695 9793 9891 9989 10087 10185 10283 10381 10479 10577
## [23869] 10675 10773 10871 10969 11067 11165 11263 11361 11459 11557 11655 11753
## [23881] 11851 11949 12047 12145 12243 12341 12439 12537 12635 12733 12831 12929
## [23893] 13027 13125 13223 13321 13419 13517 13615 13713 13811 13909 14007 14105
## [23905] 14203 14301 14399 14497 14595 14693 14791 14889 14987 15085 15183 15281
## [23917] 15379 15477 15575 15673 15771 15869 15967 16065 16163 16261 16359 16457
## [23929] 16555 16653 16751 16849 16947 17045 17143 17241 17339 17437 17535 17633
## [23941] 17731 17829 17927 18025 18123 18221 18319 18417 18515 18613 18711 18809
## [23953] 18907 19005 19103 19201 19299 19397 19495 19593 19691 19789 19887 19985
## [23965] 20083 20181 20279 20377 20475 20573 20671 20769 20867 20965 21063 21161
## [23977] 21259 21357 21455 21553 21651 21749 21847 21945 22043 22141 22239 22337
## [23989] 22435 22533 22631 22729 22827 22925 23023 23121 23219 23317 23415 23513
## [24001] 23611 23709 23807 23905 24003 24101 24199 24297 24395 24493 24591 24689
## [24013] 24787 24885 24983 25081 25179 25277 25375 25473 25571 25669 25767 25865
## [24025] 92 190 288 386 484 582 680 778 876 974 1072 1170
## [24037] 1268 1366 1464 1562 1660 1758 1856 1954 2052 2150 2248 2346
## [24049] 2444 2542 2640 2738 2836 2934 3032 3130 3228 3326 3424 3522
## [24061] 3620 3718 3816 3914 4012 4110 4208 4306 4404 4502 4600 4698
## [24073] 4796 4894 4992 5090 5188 5286 5384 5482 5580 5678 5776 5874
## [24085] 5972 6070 6168 6266 6364 6462 6560 6658 6756 6854 6952 7050
## [24097] 7148 7246 7344 7442 7540 7638 7736 7834 7932 8030 8128 8226
## [24109] 8324 8422 8520 8618 8716 8814 8912 9010 9108 9206 9304 9402
## [24121] 9500 9598 9696 9794 9892 9990 10088 10186 10284 10382 10480 10578
## [24133] 10676 10774 10872 10970 11068 11166 11264 11362 11460 11558 11656 11754
## [24145] 11852 11950 12048 12146 12244 12342 12440 12538 12636 12734 12832 12930
## [24157] 13028 13126 13224 13322 13420 13518 13616 13714 13812 13910 14008 14106
## [24169] 14204 14302 14400 14498 14596 14694 14792 14890 14988 15086 15184 15282
## [24181] 15380 15478 15576 15674 15772 15870 15968 16066 16164 16262 16360 16458
## [24193] 16556 16654 16752 16850 16948 17046 17144 17242 17340 17438 17536 17634
## [24205] 17732 17830 17928 18026 18124 18222 18320 18418 18516 18614 18712 18810
## [24217] 18908 19006 19104 19202 19300 19398 19496 19594 19692 19790 19888 19986
## [24229] 20084 20182 20280 20378 20476 20574 20672 20770 20868 20966 21064 21162
## [24241] 21260 21358 21456 21554 21652 21750 21848 21946 22044 22142 22240 22338
## [24253] 22436 22534 22632 22730 22828 22926 23024 23122 23220 23318 23416 23514
## [24265] 23612 23710 23808 23906 24004 24102 24200 24298 24396 24494 24592 24690
## [24277] 24788 24886 24984 25082 25180 25278 25376 25474 25572 25670 25768 25866
## [24289] 93 191 289 387 485 583 681 779 877 975 1073 1171
## [24301] 1269 1367 1465 1563 1661 1759 1857 1955 2053 2151 2249 2347
## [24313] 2445 2543 2641 2739 2837 2935 3033 3131 3229 3327 3425 3523
## [24325] 3621 3719 3817 3915 4013 4111 4209 4307 4405 4503 4601 4699
## [24337] 4797 4895 4993 5091 5189 5287 5385 5483 5581 5679 5777 5875
## [24349] 5973 6071 6169 6267 6365 6463 6561 6659 6757 6855 6953 7051
## [24361] 7149 7247 7345 7443 7541 7639 7737 7835 7933 8031 8129 8227
## [24373] 8325 8423 8521 8619 8717 8815 8913 9011 9109 9207 9305 9403
## [24385] 9501 9599 9697 9795 9893 9991 10089 10187 10285 10383 10481 10579
## [24397] 10677 10775 10873 10971 11069 11167 11265 11363 11461 11559 11657 11755
## [24409] 11853 11951 12049 12147 12245 12343 12441 12539 12637 12735 12833 12931
## [24421] 13029 13127 13225 13323 13421 13519 13617 13715 13813 13911 14009 14107
## [24433] 14205 14303 14401 14499 14597 14695 14793 14891 14989 15087 15185 15283
## [24445] 15381 15479 15577 15675 15773 15871 15969 16067 16165 16263 16361 16459
## [24457] 16557 16655 16753 16851 16949 17047 17145 17243 17341 17439 17537 17635
## [24469] 17733 17831 17929 18027 18125 18223 18321 18419 18517 18615 18713 18811
## [24481] 18909 19007 19105 19203 19301 19399 19497 19595 19693 19791 19889 19987
## [24493] 20085 20183 20281 20379 20477 20575 20673 20771 20869 20967 21065 21163
## [24505] 21261 21359 21457 21555 21653 21751 21849 21947 22045 22143 22241 22339
## [24517] 22437 22535 22633 22731 22829 22927 23025 23123 23221 23319 23417 23515
## [24529] 23613 23711 23809 23907 24005 24103 24201 24299 24397 24495 24593 24691
## [24541] 24789 24887 24985 25083 25181 25279 25377 25475 25573 25671 25769 25867
## [24553] 94 192 290 388 486 584 682 780 878 976 1074 1172
## [24565] 1270 1368 1466 1564 1662 1760 1858 1956 2054 2152 2250 2348
## [24577] 2446 2544 2642 2740 2838 2936 3034 3132 3230 3328 3426 3524
## [24589] 3622 3720 3818 3916 4014 4112 4210 4308 4406 4504 4602 4700
## [24601] 4798 4896 4994 5092 5190 5288 5386 5484 5582 5680 5778 5876
## [24613] 5974 6072 6170 6268 6366 6464 6562 6660 6758 6856 6954 7052
## [24625] 7150 7248 7346 7444 7542 7640 7738 7836 7934 8032 8130 8228
## [24637] 8326 8424 8522 8620 8718 8816 8914 9012 9110 9208 9306 9404
## [24649] 9502 9600 9698 9796 9894 9992 10090 10188 10286 10384 10482 10580
## [24661] 10678 10776 10874 10972 11070 11168 11266 11364 11462 11560 11658 11756
## [24673] 11854 11952 12050 12148 12246 12344 12442 12540 12638 12736 12834 12932
## [24685] 13030 13128 13226 13324 13422 13520 13618 13716 13814 13912 14010 14108
## [24697] 14206 14304 14402 14500 14598 14696 14794 14892 14990 15088 15186 15284
## [24709] 15382 15480 15578 15676 15774 15872 15970 16068 16166 16264 16362 16460
## [24721] 16558 16656 16754 16852 16950 17048 17146 17244 17342 17440 17538 17636
## [24733] 17734 17832 17930 18028 18126 18224 18322 18420 18518 18616 18714 18812
## [24745] 18910 19008 19106 19204 19302 19400 19498 19596 19694 19792 19890 19988
## [24757] 20086 20184 20282 20380 20478 20576 20674 20772 20870 20968 21066 21164
## [24769] 21262 21360 21458 21556 21654 21752 21850 21948 22046 22144 22242 22340
## [24781] 22438 22536 22634 22732 22830 22928 23026 23124 23222 23320 23418 23516
## [24793] 23614 23712 23810 23908 24006 24104 24202 24300 24398 24496 24594 24692
## [24805] 24790 24888 24986 25084 25182 25280 25378 25476 25574 25672 25770 25868
## [24817] 95 193 291 389 487 585 683 781 879 977 1075 1173
## [24829] 1271 1369 1467 1565 1663 1761 1859 1957 2055 2153 2251 2349
## [24841] 2447 2545 2643 2741 2839 2937 3035 3133 3231 3329 3427 3525
## [24853] 3623 3721 3819 3917 4015 4113 4211 4309 4407 4505 4603 4701
## [24865] 4799 4897 4995 5093 5191 5289 5387 5485 5583 5681 5779 5877
## [24877] 5975 6073 6171 6269 6367 6465 6563 6661 6759 6857 6955 7053
## [24889] 7151 7249 7347 7445 7543 7641 7739 7837 7935 8033 8131 8229
## [24901] 8327 8425 8523 8621 8719 8817 8915 9013 9111 9209 9307 9405
## [24913] 9503 9601 9699 9797 9895 9993 10091 10189 10287 10385 10483 10581
## [24925] 10679 10777 10875 10973 11071 11169 11267 11365 11463 11561 11659 11757
## [24937] 11855 11953 12051 12149 12247 12345 12443 12541 12639 12737 12835 12933
## [24949] 13031 13129 13227 13325 13423 13521 13619 13717 13815 13913 14011 14109
## [24961] 14207 14305 14403 14501 14599 14697 14795 14893 14991 15089 15187 15285
## [24973] 15383 15481 15579 15677 15775 15873 15971 16069 16167 16265 16363 16461
## [24985] 16559 16657 16755 16853 16951 17049 17147 17245 17343 17441 17539 17637
## [24997] 17735 17833 17931 18029 18127 18225 18323 18421 18519 18617 18715 18813
## [25009] 18911 19009 19107 19205 19303 19401 19499 19597 19695 19793 19891 19989
## [25021] 20087 20185 20283 20381 20479 20577 20675 20773 20871 20969 21067 21165
## [25033] 21263 21361 21459 21557 21655 21753 21851 21949 22047 22145 22243 22341
## [25045] 22439 22537 22635 22733 22831 22929 23027 23125 23223 23321 23419 23517
## [25057] 23615 23713 23811 23909 24007 24105 24203 24301 24399 24497 24595 24693
## [25069] 24791 24889 24987 25085 25183 25281 25379 25477 25575 25673 25771 25869
## [25081] 96 194 292 390 488 586 684 782 880 978 1076 1174
## [25093] 1272 1370 1468 1566 1664 1762 1860 1958 2056 2154 2252 2350
## [25105] 2448 2546 2644 2742 2840 2938 3036 3134 3232 3330 3428 3526
## [25117] 3624 3722 3820 3918 4016 4114 4212 4310 4408 4506 4604 4702
## [25129] 4800 4898 4996 5094 5192 5290 5388 5486 5584 5682 5780 5878
## [25141] 5976 6074 6172 6270 6368 6466 6564 6662 6760 6858 6956 7054
## [25153] 7152 7250 7348 7446 7544 7642 7740 7838 7936 8034 8132 8230
## [25165] 8328 8426 8524 8622 8720 8818 8916 9014 9112 9210 9308 9406
## [25177] 9504 9602 9700 9798 9896 9994 10092 10190 10288 10386 10484 10582
## [25189] 10680 10778 10876 10974 11072 11170 11268 11366 11464 11562 11660 11758
## [25201] 11856 11954 12052 12150 12248 12346 12444 12542 12640 12738 12836 12934
## [25213] 13032 13130 13228 13326 13424 13522 13620 13718 13816 13914 14012 14110
## [25225] 14208 14306 14404 14502 14600 14698 14796 14894 14992 15090 15188 15286
## [25237] 15384 15482 15580 15678 15776 15874 15972 16070 16168 16266 16364 16462
## [25249] 16560 16658 16756 16854 16952 17050 17148 17246 17344 17442 17540 17638
## [25261] 17736 17834 17932 18030 18128 18226 18324 18422 18520 18618 18716 18814
## [25273] 18912 19010 19108 19206 19304 19402 19500 19598 19696 19794 19892 19990
## [25285] 20088 20186 20284 20382 20480 20578 20676 20774 20872 20970 21068 21166
## [25297] 21264 21362 21460 21558 21656 21754 21852 21950 22048 22146 22244 22342
## [25309] 22440 22538 22636 22734 22832 22930 23028 23126 23224 23322 23420 23518
## [25321] 23616 23714 23812 23910 24008 24106 24204 24302 24400 24498 24596 24694
## [25333] 24792 24890 24988 25086 25184 25282 25380 25478 25576 25674 25772 25870
## [25345] 97 195 293 391 489 587 685 783 881 979 1077 1175
## [25357] 1273 1371 1469 1567 1665 1763 1861 1959 2057 2155 2253 2351
## [25369] 2449 2547 2645 2743 2841 2939 3037 3135 3233 3331 3429 3527
## [25381] 3625 3723 3821 3919 4017 4115 4213 4311 4409 4507 4605 4703
## [25393] 4801 4899 4997 5095 5193 5291 5389 5487 5585 5683 5781 5879
## [25405] 5977 6075 6173 6271 6369 6467 6565 6663 6761 6859 6957 7055
## [25417] 7153 7251 7349 7447 7545 7643 7741 7839 7937 8035 8133 8231
## [25429] 8329 8427 8525 8623 8721 8819 8917 9015 9113 9211 9309 9407
## [25441] 9505 9603 9701 9799 9897 9995 10093 10191 10289 10387 10485 10583
## [25453] 10681 10779 10877 10975 11073 11171 11269 11367 11465 11563 11661 11759
## [25465] 11857 11955 12053 12151 12249 12347 12445 12543 12641 12739 12837 12935
## [25477] 13033 13131 13229 13327 13425 13523 13621 13719 13817 13915 14013 14111
## [25489] 14209 14307 14405 14503 14601 14699 14797 14895 14993 15091 15189 15287
## [25501] 15385 15483 15581 15679 15777 15875 15973 16071 16169 16267 16365 16463
## [25513] 16561 16659 16757 16855 16953 17051 17149 17247 17345 17443 17541 17639
## [25525] 17737 17835 17933 18031 18129 18227 18325 18423 18521 18619 18717 18815
## [25537] 18913 19011 19109 19207 19305 19403 19501 19599 19697 19795 19893 19991
## [25549] 20089 20187 20285 20383 20481 20579 20677 20775 20873 20971 21069 21167
## [25561] 21265 21363 21461 21559 21657 21755 21853 21951 22049 22147 22245 22343
## [25573] 22441 22539 22637 22735 22833 22931 23029 23127 23225 23323 23421 23519
## [25585] 23617 23715 23813 23911 24009 24107 24205 24303 24401 24499 24597 24695
## [25597] 24793 24891 24989 25087 25185 25283 25381 25479 25577 25675 25773 25871
## [25609] 98 196 294 392 490 588 686 784 882 980 1078 1176
## [25621] 1274 1372 1470 1568 1666 1764 1862 1960 2058 2156 2254 2352
## [25633] 2450 2548 2646 2744 2842 2940 3038 3136 3234 3332 3430 3528
## [25645] 3626 3724 3822 3920 4018 4116 4214 4312 4410 4508 4606 4704
## [25657] 4802 4900 4998 5096 5194 5292 5390 5488 5586 5684 5782 5880
## [25669] 5978 6076 6174 6272 6370 6468 6566 6664 6762 6860 6958 7056
## [25681] 7154 7252 7350 7448 7546 7644 7742 7840 7938 8036 8134 8232
## [25693] 8330 8428 8526 8624 8722 8820 8918 9016 9114 9212 9310 9408
## [25705] 9506 9604 9702 9800 9898 9996 10094 10192 10290 10388 10486 10584
## [25717] 10682 10780 10878 10976 11074 11172 11270 11368 11466 11564 11662 11760
## [25729] 11858 11956 12054 12152 12250 12348 12446 12544 12642 12740 12838 12936
## [25741] 13034 13132 13230 13328 13426 13524 13622 13720 13818 13916 14014 14112
## [25753] 14210 14308 14406 14504 14602 14700 14798 14896 14994 15092 15190 15288
## [25765] 15386 15484 15582 15680 15778 15876 15974 16072 16170 16268 16366 16464
## [25777] 16562 16660 16758 16856 16954 17052 17150 17248 17346 17444 17542 17640
## [25789] 17738 17836 17934 18032 18130 18228 18326 18424 18522 18620 18718 18816
## [25801] 18914 19012 19110 19208 19306 19404 19502 19600 19698 19796 19894 19992
## [25813] 20090 20188 20286 20384 20482 20580 20678 20776 20874 20972 21070 21168
## [25825] 21266 21364 21462 21560 21658 21756 21854 21952 22050 22148 22246 22344
## [25837] 22442 22540 22638 22736 22834 22932 23030 23128 23226 23324 23422 23520
## [25849] 23618 23716 23814 23912 24010 24108 24206 24304 24402 24500 24598 24696
## [25861] 24794 24892 24990 25088 25186 25284 25382 25480 25578 25676 25774 25872
# I then merge the cleaned deceased and confirmed datasets.
Merged_Data <- cbind(Confirmed_Long, Deceased_Long)
colnames(Merged_Data)
## [1] "Province/State" "Country/Region" "Lat" "Long"
## [5] "Dates" "Confirmed_Cases" "COUNTRY" "Date"
## [9] "Province/State" "Country/Region" "Lat" "Long"
## [13] "Dates" "Deceased" "COUNTRY" "Date"
# I only keep the variables we are interested in.
myvars <- c("COUNTRY", "Province/State", "Lat", "Long", "Dates", "Confirmed_Cases", "Date", "Deceased")
# I rename this file as our temporary data.
Temporary_Data_Country <- Merged_Data[myvars]
# We might also want a dataset with the total number of global confirmed cases and deaths per day.
World_Data <- Temporary_Data_Country %>% group_by(Date) %>%
summarise(country='World', Global_Confirmed_Cases = sum(Confirmed_Cases, na.rm=T),
Global_Deceased = sum(Deceased, na.rm=T))
World_Data$Death_Rate <- ((World_Data$Global_Deceased/World_Data$Global_Confirmed_Cases) * 100)
# Here, I subset the data so as to properly collapse by country.
myvarstemporary <- c("COUNTRY", "Date", "Confirmed_Cases", "Deceased")
Temporary_Data_Country <- Temporary_Data_Country[myvarstemporary]
Temporary_Data_Country <- Temporary_Data_Country %>%
group_by(COUNTRY, Date) %>%
summarise(Total_Cases_Country = sum(Confirmed_Cases), Total_Deceased_Country = sum(Deceased))
# I next want to calculate the increase in deaths and confirmed cases per day.
Temporary_Data_Country %>%
arrange(COUNTRY, Date)
## # A tibble: 18,130 x 4
## # Groups: COUNTRY [185]
## COUNTRY Date Total_Cases_Country Total_Deceased_Country
## <chr> <date> <dbl> <dbl>
## 1 Afghanistan 2020-01-22 0 0
## 2 Afghanistan 2020-01-23 0 0
## 3 Afghanistan 2020-01-24 0 0
## 4 Afghanistan 2020-01-25 0 0
## 5 Afghanistan 2020-01-26 0 0
## 6 Afghanistan 2020-01-27 0 0
## 7 Afghanistan 2020-01-28 0 0
## 8 Afghanistan 2020-01-29 0 0
## 9 Afghanistan 2020-01-30 0 0
## 10 Afghanistan 2020-01-31 0 0
## # … with 18,120 more rows
number <- nrow(Temporary_Data_Country)
First_Day <- min(Temporary_Data_Country$Date)
Temporary_Data_Country$New_Confirmed_Country <- ifelse(Temporary_Data_Country$Date == First_Day, NA, Temporary_Data_Country$Total_Cases_Country - dplyr::lag(Temporary_Data_Country$Total_Cases_Country, number=1))
Temporary_Data_Country$New_Total_Deceased_Country <- ifelse(Temporary_Data_Country$Date == First_Day, NA, Temporary_Data_Country$Total_Deceased_Country - dplyr::lag(Temporary_Data_Country$Total_Deceased_Country, number=1))
# There are a few values for which we have negative new deaths and new cases. Intuitively, this shouldn't make sense. This is actually a problem which steps from the COVID data. For example, Iceland has some weird values in the initial dataframe. Although deaths should represent cumulative sums, if we observe the data from 3/15 - 4/05, we note that the number of recorded deaths drops from 5 to 0, then increases back to six as the month progresses. This is a well-documented problem which stems from the original Github data: https://github.com/CSSEGISandData/COVID-19/issues/2379; https://github.com/CSSEGISandData/COVID-19/issues/2165. We can either exclude data points which show a decrease in cumulative deaths (which I haven't done here); or wait for the data to be updated. Because we only drop a few observations from the analysis, I choose to exclude them, and proceed as normal. Note that when we take global sums, this will affect our cumulative totals (as we have excluded observations for certain nations on certain days).
Temporary_Data_Country <- Temporary_Data_Country[which(Temporary_Data_Country$New_Confirmed_Country >= 0),]
Temporary_Data_Country <- Temporary_Data_Country[which(Temporary_Data_Country$New_Total_Deceased_Country >= 0),]
# Now, I calculate the total mortality rate per capita, new mortality rate per capita, death rate per capita, and confirmed cases per capita (using data from the United Nations on global demographics for the merge).
UN_Population <- read_csv("data/UN_Population_Data.csv")
## Parsed with column specification:
## cols(
## Location = col_character(),
## Population = col_double()
## )
UN_Population$COUNTRY <- UN_Population$Location
UN_Population[25, "COUNTRY"] <- "Bolivia"
UN_Population[30, "COUNTRY"] <- "Brunei"
UN_Population[43, "COUNTRY"] <- "Taiwan*"
UN_Population[46, "COUNTRY"] <- "Congo (Brazzaville)"
UN_Population[54, "COUNTRY"] <-"Cote d'Ivoire"
UN_Population[56, "COUNTRY"] <- "Congo (Kinshasa)"
UN_Population[98, "COUNTRY"] <- "Iran"
UN_Population[112, "COUNTRY"] <- "Laos"
UN_Population[138, "COUNTRY"] <- "Burma"
UN_Population[162, "COUNTRY"] <- "Korea, South"
UN_Population[163, "COUNTRY"] <- "Moldova"
UN_Population[165, "COUNTRY"] <- "Russia"
UN_Population[196, "COUNTRY"] <- "Syria"
UN_Population[212, "COUNTRY"] <- "Tanzania"
UN_Population[213, "COUNTRY"] <- "US"
UN_Population[217, "COUNTRY"] <- "Venezuela"
UN_Population[218, "COUNTRY"] <- "Vietnam"
# I subset the file to only include the nations in our final data.
UN_Population <- subset(UN_Population, is.element(UN_Population$COUNTRY, Temporary_Data_Country$COUNTRY))
# Lastly, I join the two dataframes. Note, we lose some observations using inner join (Western Sahara, West Bank and Gaza, MS Zaandam, Kosovo, Diamond Princess, and the Central African Republic.). However, we are not interested in these nations for the purposes of our sample.
Temporary_Data_Country <- inner_join(Temporary_Data_Country, UN_Population)
## Joining, by = "COUNTRY"
# This allows me to calculate our variable of interest: the mortality rate per capita (for both total deaths, and new deaths).
Temporary_Data_Country$Total_Mortality_Rate_Per_Capita = (Temporary_Data_Country$Total_Deceased_Country / Temporary_Data_Country$Population)
Temporary_Data_Country$New_Mortality_Rate_Per_Capita = (Temporary_Data_Country$New_Total_Deceased_Country / Temporary_Data_Country$Population)
# We might also be interested in cases per capita.
Temporary_Data_Country$Total_Cases_Country_Per_Capita = (Temporary_Data_Country$Total_Cases_Country / Temporary_Data_Country$Population)
# Lastly, I want to create a rolling average of the total and new mortality rates per capita, new confirmed cases, and new deaths. This is a bit complicated, so bear with me.
# First, I order the data by country and date.
Temporary_Data_Country <- Temporary_Data_Country[order(Temporary_Data_Country$COUNTRY, Temporary_Data_Country$Date),]
# I then construct a seven-day rolling average of confirmed cases, deaths, and both types of mortality rates by country.
Temporary_Data_Country <- Temporary_Data_Country %>%
group_by(COUNTRY) %>%
mutate(rolling_average_confirmed = frollmean(New_Confirmed_Country, 7))
Temporary_Data_Country <- Temporary_Data_Country %>%
group_by(COUNTRY) %>%
mutate(rolling_average_deceased= frollmean(New_Total_Deceased_Country, 7))
Temporary_Data_Country <- Temporary_Data_Country %>%
group_by(COUNTRY) %>%
mutate(total_rolling_average_mortality = frollmean(Total_Mortality_Rate_Per_Capita, 7))
Temporary_Data_Country <- Temporary_Data_Country %>%
group_by(COUNTRY) %>%
mutate(new_rolling_average_mortality = frollmean(New_Mortality_Rate_Per_Capita, 7))
# The seven-day rolling average works well, but the algorithm only gives an output after the first week (as we need seven prior values for the sake of our calculation). Ideally, we would want these missing values to have their own rolling averages (i.e. a rolling average of two days for the second date in our dataset; or a rolling average of five days for the fifth date). To do this, I first create a new dataframe with our missing values from the initial seven-day rolling average.
Missing_Mean_Confirmed <- Temporary_Data_Country[which(is.na(Temporary_Data_Country$rolling_average_confirmed)), ]
Missing_Mean_Total_Deceased_Country <- Temporary_Data_Country[which(is.na(Temporary_Data_Country$rolling_average_deceased)), ]
Missing_Mean_Total_Mortality <- Temporary_Data_Country[which(is.na(Temporary_Data_Country$total_rolling_average_mortality)), ]
Missing_Mean_New_Mortality <- Temporary_Data_Country[which(is.na(Temporary_Data_Country$new_rolling_average_mortality)), ]
# Next, I calculate a "new" rolling average for each of our nations, using the roll apply function. The syntax is complicated; but essentially this command allows the user the freedom to vary rolling averages by different "windows" of dates (i.e. three-day average vs. six-day average).
Missing_Mean_Confirmed <- Missing_Mean_Confirmed %>%
group_by(COUNTRY) %>%
mutate(rolling_average_confirmed2 = rollapply(New_Confirmed_Country, 6, mean, na.rm = TRUE, fill = NA, align = 'right', partial=TRUE))
Missing_Mean_Total_Deceased_Country <- Missing_Mean_Total_Deceased_Country %>%
group_by(COUNTRY) %>%
mutate(rolling_average_deceased2 = rollapply(New_Total_Deceased_Country, 6, mean, na.rm = TRUE, fill = NA, align = 'right', partial=TRUE))
Missing_Mean_Total_Mortality <- Missing_Mean_Total_Mortality %>%
group_by(COUNTRY) %>%
mutate(total_rolling_average_mortality2 = rollapply(Total_Mortality_Rate_Per_Capita, 6, mean, na.rm = TRUE, fill = NA, align = 'right', partial=TRUE))
Missing_Mean_New_Mortality <- Missing_Mean_New_Mortality %>%
group_by(COUNTRY) %>%
mutate(new_rolling_average_mortality2 = rollapply(New_Mortality_Rate_Per_Capita, 6, mean, na.rm = TRUE, fill = NA, align = 'right', partial=TRUE))
# We now want to merge this data back to our temporary file.
Temporary_Data_Country <- merge(Temporary_Data_Country, Missing_Mean_Confirmed, by = c("COUNTRY", "Date"), all=TRUE)
Temporary_Data_Country <- merge(Temporary_Data_Country, Missing_Mean_Total_Deceased_Country, by = c("COUNTRY", "Date"), all=TRUE)
Temporary_Data_Country <- merge(Temporary_Data_Country, Missing_Mean_Total_Mortality, by = c("COUNTRY", "Date"), all=TRUE)
## Warning in merge.data.frame(Temporary_Data_Country,
## Missing_Mean_Total_Mortality, : column names 'Total_Cases_Country.x',
## 'Total_Deceased_Country.x', 'New_Confirmed_Country.x',
## 'New_Total_Deceased_Country.x', 'Location.x', 'Population.x',
## 'Total_Mortality_Rate_Per_Capita.x', 'New_Mortality_Rate_Per_Capita.x',
## 'Total_Cases_Country_Per_Capita.x', 'rolling_average_confirmed.x',
## 'rolling_average_deceased.x', 'total_rolling_average_mortality.x',
## 'new_rolling_average_mortality.x', 'Total_Cases_Country.y',
## 'Total_Deceased_Country.y', 'New_Confirmed_Country.y',
## 'New_Total_Deceased_Country.y', 'Location.y', 'Population.y',
## 'Total_Mortality_Rate_Per_Capita.y', 'New_Mortality_Rate_Per_Capita.y',
## 'Total_Cases_Country_Per_Capita.y', 'rolling_average_confirmed.y',
## 'rolling_average_deceased.y', 'total_rolling_average_mortality.y',
## 'new_rolling_average_mortality.y' are duplicated in the result
Temporary_Data_Country <- merge(Temporary_Data_Country, Missing_Mean_New_Mortality, by = c("COUNTRY", "Date"), all=TRUE)
## Warning in merge.data.frame(Temporary_Data_Country,
## Missing_Mean_New_Mortality, : column names 'Total_Cases_Country.x',
## 'Total_Deceased_Country.x', 'New_Confirmed_Country.x',
## 'New_Total_Deceased_Country.x', 'Location.x', 'Population.x',
## 'Total_Mortality_Rate_Per_Capita.x', 'New_Mortality_Rate_Per_Capita.x',
## 'Total_Cases_Country_Per_Capita.x', 'rolling_average_confirmed.x',
## 'rolling_average_deceased.x', 'total_rolling_average_mortality.x',
## 'new_rolling_average_mortality.x', 'Total_Cases_Country.y',
## 'Total_Deceased_Country.y', 'New_Confirmed_Country.y',
## 'New_Total_Deceased_Country.y', 'Location.y', 'Population.y',
## 'Total_Mortality_Rate_Per_Capita.y', 'New_Mortality_Rate_Per_Capita.y',
## 'Total_Cases_Country_Per_Capita.y', 'rolling_average_confirmed.y',
## 'rolling_average_deceased.y', 'total_rolling_average_mortality.y',
## 'new_rolling_average_mortality.y' are duplicated in the result
# Lastly, we can replace the missing values in our temporary file (for the confirmed rolling averages prior to one week) with the newly calculated "partial" rolling averages. We then replace all missing values with zero (the only missing values which remain are for our first day, when naturally no new cases will be recorded, or, in the case of our rolling average for the mortality rate, for dates in which we don't yet have a recorded case).
Temporary_Data_Country$rolling_average_confirmed.x[is.na(Temporary_Data_Country$rolling_average_confirmed.x)] <- Temporary_Data_Country$rolling_average_confirmed2[is.na(Temporary_Data_Country$rolling_average_confirmed.x)]
Temporary_Data_Country$rolling_average_confirmed.x[is.na(Temporary_Data_Country$rolling_average_confirmed.x)] <- 0
Temporary_Data_Country$rolling_average_confirmed <- Temporary_Data_Country$rolling_average_confirmed.x
Temporary_Data_Country$rolling_average_deceased.x[is.na(Temporary_Data_Country$rolling_average_deceased.x)] <- Temporary_Data_Country$rolling_average_deceased2[is.na(Temporary_Data_Country$rolling_average_deceased.x)]
Temporary_Data_Country$rolling_average_deceased.x[is.na(Temporary_Data_Country$rolling_average_deceased.x)] <- 0
Temporary_Data_Country$rolling_average_deceased <- Temporary_Data_Country$rolling_average_deceased.x
Temporary_Data_Country$total_rolling_average_mortality.x[is.na(Temporary_Data_Country$total_rolling_average_mortality.x)] <- Temporary_Data_Country$total_rolling_average_mortality2[is.na(Temporary_Data_Country$total_rolling_average_mortality.x)]
Temporary_Data_Country$total_rolling_average_mortality.x[is.na(Temporary_Data_Country$total_rolling_average_mortality.x)] <- 0
Temporary_Data_Country$total_rolling_average_mortality <- Temporary_Data_Country$total_rolling_average_mortality.x
Temporary_Data_Country$new_rolling_average_mortality.x[is.na(Temporary_Data_Country$new_rolling_average_mortality.x)] <- Temporary_Data_Country$new_rolling_average_mortality2[is.na(Temporary_Data_Country$new_rolling_average_mortality.x)]
Temporary_Data_Country$new_rolling_average_mortality.x[is.na(Temporary_Data_Country$new_rolling_average_mortality.x)] <- 0
Temporary_Data_Country$new_rolling_average_mortality <- Temporary_Data_Country$new_rolling_average_mortality.x
# I only keep columns we care about (which are not repetitive).
Temporary_Data_Country$Total_Cases_Country <- Temporary_Data_Country$Total_Cases_Country.x
Temporary_Data_Country$Total_Deceased_Country <- Temporary_Data_Country$Total_Deceased_Country.x
Temporary_Data_Country$New_Confirmed_Country <- Temporary_Data_Country$New_Confirmed_Country.x
Temporary_Data_Country$New_Total_Deceased_Country <- Temporary_Data_Country$New_Total_Deceased_Country.x
Temporary_Data_Country$Death_Rate <- Temporary_Data_Country$Death_Rate.x
Temporary_Data_Country$Total_Mortality_Rate_Per_Capita <- Temporary_Data_Country$Total_Mortality_Rate_Per_Capita.x
Temporary_Data_Country$New_Mortality_Rate_Per_Capita <- Temporary_Data_Country$New_Mortality_Rate_Per_Capita.x
Temporary_Data_Country$Location <- Temporary_Data_Country$Location.x
Temporary_Data_Country$Population <- Temporary_Data_Country$Population.x
Temporary_Data_Country$Total_Cases_Country_Per_Capita <- Temporary_Data_Country$Total_Cases_Country_Per_Capita.x
Temporary_Data_Country$rolling_average_confirmed <- Temporary_Data_Country$rolling_average_confirmed.x
Temporary_Data_Country$rolling_average_deceased <- Temporary_Data_Country$rolling_average_deceased.x
Temporary_Data_Country$total_rolling_average_mortality <- Temporary_Data_Country$total_rolling_average_mortality.x
Temporary_Data_Country$new_rolling_average_mortality <- Temporary_Data_Country$new_rolling_average_mortality.x
colnames(Temporary_Data_Country)
## [1] "COUNTRY" "Date"
## [3] "Total_Cases_Country.x" "Total_Deceased_Country.x"
## [5] "New_Confirmed_Country.x" "New_Total_Deceased_Country.x"
## [7] "Location.x" "Population.x"
## [9] "Total_Mortality_Rate_Per_Capita.x" "New_Mortality_Rate_Per_Capita.x"
## [11] "Total_Cases_Country_Per_Capita.x" "rolling_average_confirmed.x"
## [13] "rolling_average_deceased.x" "total_rolling_average_mortality.x"
## [15] "new_rolling_average_mortality.x" "Total_Cases_Country.y"
## [17] "Total_Deceased_Country.y" "New_Confirmed_Country.y"
## [19] "New_Total_Deceased_Country.y" "Location.y"
## [21] "Population.y" "Total_Mortality_Rate_Per_Capita.y"
## [23] "New_Mortality_Rate_Per_Capita.y" "Total_Cases_Country_Per_Capita.y"
## [25] "rolling_average_confirmed.y" "rolling_average_deceased.y"
## [27] "total_rolling_average_mortality.y" "new_rolling_average_mortality.y"
## [29] "rolling_average_confirmed2" "Total_Cases_Country.x"
## [31] "Total_Deceased_Country.x" "New_Confirmed_Country.x"
## [33] "New_Total_Deceased_Country.x" "Location.x"
## [35] "Population.x" "Total_Mortality_Rate_Per_Capita.x"
## [37] "New_Mortality_Rate_Per_Capita.x" "Total_Cases_Country_Per_Capita.x"
## [39] "rolling_average_confirmed.x" "rolling_average_deceased.x"
## [41] "total_rolling_average_mortality.x" "new_rolling_average_mortality.x"
## [43] "rolling_average_deceased2" "Total_Cases_Country.y"
## [45] "Total_Deceased_Country.y" "New_Confirmed_Country.y"
## [47] "New_Total_Deceased_Country.y" "Location.y"
## [49] "Population.y" "Total_Mortality_Rate_Per_Capita.y"
## [51] "New_Mortality_Rate_Per_Capita.y" "Total_Cases_Country_Per_Capita.y"
## [53] "rolling_average_confirmed.y" "rolling_average_deceased.y"
## [55] "total_rolling_average_mortality.y" "new_rolling_average_mortality.y"
## [57] "total_rolling_average_mortality2" "Total_Cases_Country"
## [59] "Total_Deceased_Country" "New_Confirmed_Country"
## [61] "New_Total_Deceased_Country" "Location"
## [63] "Population" "Total_Mortality_Rate_Per_Capita"
## [65] "New_Mortality_Rate_Per_Capita" "Total_Cases_Country_Per_Capita"
## [67] "rolling_average_confirmed" "rolling_average_deceased"
## [69] "total_rolling_average_mortality" "new_rolling_average_mortality"
## [71] "new_rolling_average_mortality2"
myvarstemp <- c("COUNTRY", "Date", "Total_Cases_Country", "Total_Deceased_Country", "New_Confirmed_Country", "New_Total_Deceased_Country", "Population", "Total_Mortality_Rate_Per_Capita", "New_Mortality_Rate_Per_Capita", "Total_Cases_Country_Per_Capita", "rolling_average_confirmed", "rolling_average_deceased", "total_rolling_average_mortality", "new_rolling_average_mortality")
Temporary_Data_Country <- Temporary_Data_Country[myvarstemp]
colnames(Temporary_Data_Country)
## [1] "COUNTRY" "Date"
## [3] "Total_Cases_Country" "Total_Deceased_Country"
## [5] "New_Confirmed_Country" "New_Total_Deceased_Country"
## [7] "Population" "Total_Mortality_Rate_Per_Capita"
## [9] "New_Mortality_Rate_Per_Capita" "Total_Cases_Country_Per_Capita"
## [11] "rolling_average_confirmed" "rolling_average_deceased"
## [13] "total_rolling_average_mortality" "new_rolling_average_mortality"
# When we collapse the COVID data to the country level, we lose our coordinate data. I now merge a dataset on global coordinates by country from: https://developers.google.com/public-data/docs/canonical/countries_csv. Note that I edited country names to match the data in the CSV itself.
Country_Coordinates <- read.csv("data/Country_Coordinates.csv")
Country_Coordinates$COUNTRY <- Country_Coordinates$Country
Country_Coordinates$latitude <- Country_Coordinates$Latitude
Country_Coordinates$longitude <- Country_Coordinates$Longitude
colnames(Country_Coordinates)
## [1] "Latitude" "Longitude" "Country" "COUNTRY" "latitude" "longitude"
myvarscoords <- c("COUNTRY", "latitude", "longitude")
Country_Coordinates <- Country_Coordinates[myvarscoords]
# Lastly, I merge the two dataframes.
Temporary_Data_Country <- merge(Temporary_Data_Country, Country_Coordinates, by = c("COUNTRY"), all=TRUE)
# I load the following two libraries in order to label our variables.
library(lattice)
library(Hmisc)
## Loading required package: survival
## Warning: package 'survival' was built under R version 3.6.2
##
## Attaching package: 'survival'
## The following object is masked from 'package:caret':
##
## cluster
## Loading required package: Formula
##
## Attaching package: 'Hmisc'
## The following object is masked from 'package:quantmod':
##
## Lag
## The following object is masked from 'package:psych':
##
## describe
## The following objects are masked from 'package:summarytools':
##
## label, label<-
## The following object is masked from 'package:plotly':
##
## subplot
## The following objects are masked from 'package:dplyr':
##
## src, summarize
## The following objects are masked from 'package:base':
##
## format.pval, units
label(Temporary_Data_Country$Total_Cases_Country) <- "Cumulative Sum of Confirmed Cases by Country (John Hopkins)"
label(Temporary_Data_Country$Total_Deceased_Country) <- "Cumulative Sum of Deaths by Country (John Hopkins)"
label(Temporary_Data_Country$New_Confirmed_Country) <- "Daily Increase in Confirmed Cases (John Hopkins)"
label(Temporary_Data_Country$New_Total_Deceased_Country) <- "Daily Increase in Deaths (John Hopkins)"
label(Temporary_Data_Country$Total_Mortality_Rate_Per_Capita) <- "Total Deaths by Population (Total_Deceased_Country/Population)"
label(Temporary_Data_Country$New_Mortality_Rate_Per_Capita) <- "New Deaths by Population (New_Total_Deceased_Country/Population)"
label(Temporary_Data_Country$Total_Cases_Country_Per_Capita) <- "Total Cases by Population (Total_Cases_Country/Population)"
label(Temporary_Data_Country$rolling_average_confirmed) <- "Seven Day Rolling Average of New Confirmed Cases by Country (with the exception of Days 1-7)"
label(Temporary_Data_Country$rolling_average_deceased) <- "Seven Day Rolling Average of New Deaths by Country (with the exception of Days 1-7)"
label(Temporary_Data_Country$total_rolling_average_mortality) <- "Seven Day Rolling Average of Total Case Mortality Rate by Country (with the exception of Days 1-7)"
label(Temporary_Data_Country$new_rolling_average_mortality) <- "Seven Day Rolling Average of New Case Mortality Rate by Country (with the exception of Days 1-7)"
# This represents the "Temporary_Data_Country.csv" file saved in the data folder. I recommend you load in the CSV directly to ensure there are no discrepancies between the edits made in the Cloud, as opposed to my own R Project. I do this below.
# Removing the Cloud version of the Temporary Data.
remove(Temporary_Data_Country)
# Importing the Temporary Data from the data folder.
Temporary_Data_Country <- read_csv("data/Temporary_Data_Country.csv")
## Parsed with column specification:
## cols(
## COUNTRY = col_character(),
## Date = col_date(format = ""),
## Total_Cases_Country = col_double(),
## Total_Deceased_Country = col_double(),
## New_Confirmed_Country = col_double(),
## New_Total_Deceased_Country = col_double(),
## Population = col_double(),
## Total_Mortality_Rate_Per_Capita = col_double(),
## New_Mortality_Rate_Per_Capita = col_double(),
## Total_Cases_Country_Per_Capita = col_double(),
## rolling_average_confirmed = col_double(),
## rolling_average_deceased = col_double(),
## total_rolling_average_mortality = col_double(),
## new_rolling_average_mortality = col_double(),
## latitude = col_double(),
## longitude = col_double()
## )
# Now, I want to merge our temporary data with the Oxford dataset on global policy responses. Please see a description of the variables here: https://www.bsg.ox.ac.uk/sites/default/files/2020-04/BSG-WP-2020-032-v5.0_0.pdf.
# The source is: Hale, Thomas, Anna Petherick, Toby Phillips, Samuel Webster. “Variation in Government Responses to COVID-19” Version 5.0. Blavatnik School of Government Working Paper. April 28, 2020. Available: www.bsg.ox.ac.uk/covidtracker.
# See https://www.bsg.ox.ac.uk/sites/default/files/Calculation%20and%20presentation%20of%20the%20Stringency%20Index.pdf for information on how the stringency index was calculated.
#
Government_Responses <- read_csv("data/Government_Responses.csv")
## Parsed with column specification:
## cols(
## .default = col_double(),
## CountryName = col_character(),
## CountryCode = col_character(),
## C1_Flag = col_logical(),
## C1_Notes = col_character(),
## C2_Flag = col_logical(),
## C2_Notes = col_character(),
## C3_Flag = col_logical(),
## C3_Notes = col_character(),
## C4_Flag = col_logical(),
## C4_Notes = col_logical(),
## C5_Notes = col_character(),
## C6_Flag = col_logical(),
## C6_Notes = col_character(),
## C7_Flag = col_logical(),
## C7_Notes = col_character(),
## C8_Notes = col_character(),
## E1_Flag = col_logical(),
## E1_Notes = col_logical(),
## E2_Notes = col_logical(),
## E3_Notes = col_character()
## # ... with 8 more columns
## )
## See spec(...) for full column specifications.
## Warning: 230 parsing failures.
## row col expected actual file
## 3112 C4_Notes 1/0/T/F/TRUE/FALSE Events were closed in February and March but no explicit limits on numbers were set.
## All public events and gatherings through the upcoming weeks were now canceled, a major challenge with one of the most celebrated holidays of the year, Tsagaan Sar, or Mongolian Lunar New Year, quickly approaching in February. All of this pre-“social distancing.”
##
## It was eventually announced that March public events would be canceled as well, including the official cancellation of the Ice Festival on Lake Khovsgol, the Winter Golden Eagle Festival, the camel festival in Umnugobi province and the Nauryz Festival nearer to the end of March. All of them major blows to the locals and the tourism dollars they rely on.
## https://web.archive.org/web/20200425111855/https://www.forbes.com/sites/breannawilson/2020/03/16/mongolia-announces-3-new-covid-19-cases-totaling-4-how-they-got-coronavirus-precautions-right/ 'data/Government_Responses.csv'
## 4558 C4_Notes 1/0/T/F/TRUE/FALSE It is not clear exactly when Wuhan started restricting gatherings. Needs more research. 'data/Government_Responses.csv'
## 4827 M1_Notes 1/0/T/F/TRUE/FALSE Vietnam declares an epidemic
## https://web.archive.org/web/20200425135141/https://vietnaminsider.vn/vietnam-government-declares-novel-coronavirus-epidemic/ 'data/Government_Responses.csv'
## 8073 C4_Notes 1/0/T/F/TRUE/FALSE https://web.archive.org/web/20200409225704/https://www.la7.it/nonelarena/video/lelenco-dei-comuni-in-quarantena-a-causa-del-coronavirus-23-02-2020-308963
##
## Localised quarantine in Lombardy 'data/Government_Responses.csv'
## 8372 C4_Notes 1/0/T/F/TRUE/FALSE The Iraqi government bans 'all gatherings in public' of any size. This applies to some gatherings labeled as private, such as funerals or weddings. https://www.reuters.com/article/us-china-health-iraq/iraq-bans-public-gatherings-on-coronavirus-fear-travel-ban-totals-nine-countries-idUSKCN20K2ZE 'data/Government_Responses.csv'
## .... ........ .................. .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... ...............................
## See problems(...) for more details.
# First, I convert the date to a proper format.
Government_Responses$Date <- ymd(Government_Responses$Date, quiet = FALSE, tz = NULL, locale = Sys.getlocale("LC_TIME"),
truncated = 0)
order(Government_Responses$Date)
## [1] 1 2 3 4 5 6 7 8 9 10 11 12
## [13] 13 14 15 16 17 18 19 20 21 22 23 24
## [25] 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48
## [49] 49 50 51 52 53 54 55 56 57 58 59 60
## [61] 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84
## [85] 85 86 87 88 89 90 91 92 93 94 95 96
## [97] 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120
## [121] 121 122 123 124 125 126 127 128 129 130 131 132
## [133] 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156
## [157] 157 158 159 160 161 162 163 164 165 166 167 168
## [169] 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192
## [193] 193 194 195 196 197 198 199 200 201 202 203 204
## [205] 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228
## [229] 229 230 231 232 233 234 235 236 237 238 239 240
## [241] 241 242 243 244 245 246 247 248 249 250 251 252
## [253] 253 254 255 256 257 258 259 260 261 262 263 264
## [265] 265 266 267 268 269 270 271 272 273 274 275 276
## [277] 277 278 279 280 281 282 283 284 285 286 287 288
## [289] 289 290 291 292 293 294 295 296 297 298 299 300
## [301] 301 302 303 304 305 306 307 308 309 310 311 312
## [313] 313 314 315 316 317 318 319 320 321 322 323 324
## [325] 325 326 327 328 329 330 331 332 333 334 335 336
## [337] 337 338 339 340 341 342 343 344 345 346 347 348
## [349] 349 350 351 352 353 354 355 356 357 358 359 360
## [361] 361 362 363 364 365 366 367 368 369 370 371 372
## [373] 373 374 375 376 377 378 379 380 381 382 383 384
## [385] 385 386 387 388 389 390 391 392 393 394 395 396
## [397] 397 398 399 400 401 402 403 404 405 406 407 408
## [409] 409 410 411 412 413 414 415 416 417 418 419 420
## [421] 421 422 423 424 425 426 427 428 429 430 431 432
## [433] 433 434 435 436 437 438 439 440 441 442 443 444
## [445] 445 446 447 448 449 450 451 452 453 454 455 456
## [457] 457 458 459 460 461 462 463 464 465 466 467 468
## [469] 469 470 471 472 473 474 475 476 477 478 479 480
## [481] 481 482 483 484 485 486 487 488 489 490 491 492
## [493] 493 494 495 496 497 498 499 500 501 502 503 504
## [505] 505 506 507 508 509 510 511 512 513 514 515 516
## [517] 517 518 519 520 521 522 523 524 525 526 527 528
## [529] 529 530 531 532 533 534 535 536 537 538 539 540
## [541] 541 542 543 544 545 546 547 548 549 550 551 552
## [553] 553 554 555 556 557 558 559 560 561 562 563 564
## [565] 565 566 567 568 569 570 571 572 573 574 575 576
## [577] 577 578 579 580 581 582 583 584 585 586 587 588
## [589] 589 590 591 592 593 594 595 596 597 598 599 600
## [601] 601 602 603 604 605 606 607 608 609 610 611 612
## [613] 613 614 615 616 617 618 619 620 621 622 623 624
## [625] 625 626 627 628 629 630 631 632 633 634 635 636
## [637] 637 638 639 640 641 642 643 644 645 646 647 648
## [649] 649 650 651 652 653 654 655 656 657 658 659 660
## [661] 661 662 663 664 665 666 667 668 669 670 671 672
## [673] 673 674 675 676 677 678 679 680 681 682 683 684
## [685] 685 686 687 688 689 690 691 692 693 694 695 696
## [697] 697 698 699 700 701 702 703 704 705 706 707 708
## [709] 709 710 711 712 713 714 715 716 717 718 719 720
## [721] 721 722 723 724 725 726 727 728 729 730 731 732
## [733] 733 734 735 736 737 738 739 740 741 742 743 744
## [745] 745 746 747 748 749 750 751 752 753 754 755 756
## [757] 757 758 759 760 761 762 763 764 765 766 767 768
## [769] 769 770 771 772 773 774 775 776 777 778 779 780
## [781] 781 782 783 784 785 786 787 788 789 790 791 792
## [793] 793 794 795 796 797 798 799 800 801 802 803 804
## [805] 805 806 807 808 809 810 811 812 813 814 815 816
## [817] 817 818 819 820 821 822 823 824 825 826 827 828
## [829] 829 830 831 832 833 834 835 836 837 838 839 840
## [841] 841 842 843 844 845 846 847 848 849 850 851 852
## [853] 853 854 855 856 857 858 859 860 861 862 863 864
## [865] 865 866 867 868 869 870 871 872 873 874 875 876
## [877] 877 878 879 880 881 882 883 884 885 886 887 888
## [889] 889 890 891 892 893 894 895 896 897 898 899 900
## [901] 901 902 903 904 905 906 907 908 909 910 911 912
## [913] 913 914 915 916 917 918 919 920 921 922 923 924
## [925] 925 926 927 928 929 930 931 932 933 934 935 936
## [937] 937 938 939 940 941 942 943 944 945 946 947 948
## [949] 949 950 951 952 953 954 955 956 957 958 959 960
## [961] 961 962 963 964 965 966 967 968 969 970 971 972
## [973] 973 974 975 976 977 978 979 980 981 982 983 984
## [985] 985 986 987 988 989 990 991 992 993 994 995 996
## [997] 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
## [1009] 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020
## [1021] 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032
## [1033] 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044
## [1045] 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
## [1057] 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068
## [1069] 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080
## [1081] 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092
## [1093] 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
## [1105] 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116
## [1117] 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128
## [1129] 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140
## [1141] 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
## [1153] 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164
## [1165] 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176
## [1177] 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188
## [1189] 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
## [1201] 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212
## [1213] 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224
## [1225] 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236
## [1237] 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
## [1249] 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260
## [1261] 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272
## [1273] 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284
## [1285] 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
## [1297] 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308
## [1309] 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320
## [1321] 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332
## [1333] 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
## [1345] 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356
## [1357] 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368
## [1369] 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380
## [1381] 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
## [1393] 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404
## [1405] 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416
## [1417] 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428
## [1429] 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
## [1441] 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452
## [1453] 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464
## [1465] 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476
## [1477] 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
## [1489] 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500
## [1501] 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512
## [1513] 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524
## [1525] 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
## [1537] 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548
## [1549] 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560
## [1561] 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572
## [1573] 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584
## [1585] 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596
## [1597] 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608
## [1609] 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620
## [1621] 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632
## [1633] 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644
## [1645] 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656
## [1657] 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668
## [1669] 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
## [1681] 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692
## [1693] 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704
## [1705] 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716
## [1717] 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
## [1729] 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740
## [1741] 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752
## [1753] 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764
## [1765] 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
## [1777] 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788
## [1789] 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800
## [1801] 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812
## [1813] 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
## [1825] 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836
## [1837] 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848
## [1849] 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860
## [1861] 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
## [1873] 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884
## [1885] 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896
## [1897] 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908
## [1909] 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
## [1921] 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932
## [1933] 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944
## [1945] 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956
## [1957] 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
## [1969] 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980
## [1981] 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992
## [1993] 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
## [2005] 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## [2017] 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028
## [2029] 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040
## [2041] 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052
## [2053] 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064
## [2065] 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076
## [2077] 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088
## [2089] 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100
## [2101] 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112
## [2113] 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124
## [2125] 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136
## [2137] 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148
## [2149] 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160
## [2161] 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172
## [2173] 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184
## [2185] 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196
## [2197] 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208
## [2209] 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220
## [2221] 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232
## [2233] 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244
## [2245] 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256
## [2257] 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268
## [2269] 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280
## [2281] 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292
## [2293] 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304
## [2305] 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316
## [2317] 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328
## [2329] 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340
## [2341] 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352
## [2353] 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364
## [2365] 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376
## [2377] 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388
## [2389] 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400
## [2401] 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412
## [2413] 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424
## [2425] 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436
## [2437] 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448
## [2449] 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460
## [2461] 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472
## [2473] 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484
## [2485] 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496
## [2497] 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508
## [2509] 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520
## [2521] 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532
## [2533] 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544
## [2545] 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556
## [2557] 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568
## [2569] 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580
## [2581] 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592
## [2593] 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604
## [2605] 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616
## [2617] 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628
## [2629] 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640
## [2641] 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652
## [2653] 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664
## [2665] 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676
## [2677] 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688
## [2689] 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700
## [2701] 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712
## [2713] 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724
## [2725] 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736
## [2737] 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748
## [2749] 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760
## [2761] 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772
## [2773] 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784
## [2785] 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796
## [2797] 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808
## [2809] 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820
## [2821] 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832
## [2833] 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844
## [2845] 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856
## [2857] 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868
## [2869] 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880
## [2881] 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892
## [2893] 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904
## [2905] 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916
## [2917] 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928
## [2929] 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940
## [2941] 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952
## [2953] 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964
## [2965] 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976
## [2977] 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988
## [2989] 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000
## [3001] 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012
## [3013] 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024
## [3025] 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036
## [3037] 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048
## [3049] 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060
## [3061] 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072
## [3073] 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084
## [3085] 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096
## [3097] 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108
## [3109] 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120
## [3121] 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132
## [3133] 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144
## [3145] 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156
## [3157] 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168
## [3169] 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180
## [3181] 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192
## [3193] 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204
## [3205] 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216
## [3217] 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228
## [3229] 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240
## [3241] 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252
## [3253] 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264
## [3265] 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276
## [3277] 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288
## [3289] 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300
## [3301] 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312
## [3313] 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324
## [3325] 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336
## [3337] 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348
## [3349] 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360
## [3361] 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372
## [3373] 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384
## [3385] 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396
## [3397] 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408
## [3409] 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 3420
## [3421] 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432
## [3433] 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444
## [3445] 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456
## [3457] 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468
## [3469] 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480
## [3481] 3481 3482 3483 3484 3485 3486 3487 3488 3489 3490 3491 3492
## [3493] 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504
## [3505] 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516
## [3517] 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528
## [3529] 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540
## [3541] 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552
## [3553] 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564
## [3565] 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576
## [3577] 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588
## [3589] 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600
## [3601] 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612
## [3613] 3613 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624
## [3625] 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634 3635 3636
## [3637] 3637 3638 3639 3640 3641 3642 3643 3644 3645 3646 3647 3648
## [3649] 3649 3650 3651 3652 3653 3654 3655 3656 3657 3658 3659 3660
## [3661] 3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671 3672
## [3673] 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684
## [3685] 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696
## [3697] 3697 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708
## [3709] 3709 3710 3711 3712 3713 3714 3715 3716 3717 3718 3719 3720
## [3721] 3721 3722 3723 3724 3725 3726 3727 3728 3729 3730 3731 3732
## [3733] 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744
## [3745] 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 3756
## [3757] 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768
## [3769] 3769 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780
## [3781] 3781 3782 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792
## [3793] 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803 3804
## [3805] 3805 3806 3807 3808 3809 3810 3811 3812 3813 3814 3815 3816
## [3817] 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 3828
## [3829] 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840
## [3841] 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852
## [3853] 3853 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864
## [3865] 3865 3866 3867 3868 3869 3870 3871 3872 3873 3874 3875 3876
## [3877] 3877 3878 3879 3880 3881 3882 3883 3884 3885 3886 3887 3888
## [3889] 3889 3890 3891 3892 3893 3894 3895 3896 3897 3898 3899 3900
## [3901] 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912
## [3913] 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924
## [3925] 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936
## [3937] 3937 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948
## [3949] 3949 3950 3951 3952 3953 3954 3955 3956 3957 3958 3959 3960
## [3961] 3961 3962 3963 3964 3965 3966 3967 3968 3969 3970 3971 3972
## [3973] 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 3984
## [3985] 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996
## [3997] 3997 3998 3999 4000 4001 4002 4003 4004 4005 4006 4007 4008
## [4009] 4009 4010 4011 4012 4013 4014 4015 4016 4017 4018 4019 4020
## [4021] 4021 4022 4023 4024 4025 4026 4027 4028 4029 4030 4031 4032
## [4033] 4033 4034 4035 4036 4037 4038 4039 4040 4041 4042 4043 4044
## [4045] 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 4056
## [4057] 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068
## [4069] 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080
## [4081] 4081 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092
## [4093] 4093 4094 4095 4096 4097 4098 4099 4100 4101 4102 4103 4104
## [4105] 4105 4106 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116
## [4117] 4117 4118 4119 4120 4121 4122 4123 4124 4125 4126 4127 4128
## [4129] 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 4140
## [4141] 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152
## [4153] 4153 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164
## [4165] 4165 4166 4167 4168 4169 4170 4171 4172 4173 4174 4175 4176
## [4177] 4177 4178 4179 4180 4181 4182 4183 4184 4185 4186 4187 4188
## [4189] 4189 4190 4191 4192 4193 4194 4195 4196 4197 4198 4199 4200
## [4201] 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212
## [4213] 4213 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224
## [4225] 4225 4226 4227 4228 4229 4230 4231 4232 4233 4234 4235 4236
## [4237] 4237 4238 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248
## [4249] 4249 4250 4251 4252 4253 4254 4255 4256 4257 4258 4259 4260
## [4261] 4261 4262 4263 4264 4265 4266 4267 4268 4269 4270 4271 4272
## [4273] 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283 4284
## [4285] 4285 4286 4287 4288 4289 4290 4291 4292 4293 4294 4295 4296
## [4297] 4297 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 4308
## [4309] 4309 4310 4311 4312 4313 4314 4315 4316 4317 4318 4319 4320
## [4321] 4321 4322 4323 4324 4325 4326 4327 4328 4329 4330 4331 4332
## [4333] 4333 4334 4335 4336 4337 4338 4339 4340 4341 4342 4343 4344
## [4345] 4345 4346 4347 4348 4349 4350 4351 4352 4353 4354 4355 4356
## [4357] 4357 4358 4359 4360 4361 4362 4363 4364 4365 4366 4367 4368
## [4369] 4369 4370 4371 4372 4373 4374 4375 4376 4377 4378 4379 4380
## [4381] 4381 4382 4383 4384 4385 4386 4387 4388 4389 4390 4391 4392
## [4393] 4393 4394 4395 4396 4397 4398 4399 4400 4401 4402 4403 4404
## [4405] 4405 4406 4407 4408 4409 4410 4411 4412 4413 4414 4415 4416
## [4417] 4417 4418 4419 4420 4421 4422 4423 4424 4425 4426 4427 4428
## [4429] 4429 4430 4431 4432 4433 4434 4435 4436 4437 4438 4439 4440
## [4441] 4441 4442 4443 4444 4445 4446 4447 4448 4449 4450 4451 4452
## [4453] 4453 4454 4455 4456 4457 4458 4459 4460 4461 4462 4463 4464
## [4465] 4465 4466 4467 4468 4469 4470 4471 4472 4473 4474 4475 4476
## [4477] 4477 4478 4479 4480 4481 4482 4483 4484 4485 4486 4487 4488
## [4489] 4489 4490 4491 4492 4493 4494 4495 4496 4497 4498 4499 4500
## [4501] 4501 4502 4503 4504 4505 4506 4507 4508 4509 4510 4511 4512
## [4513] 4513 4514 4515 4516 4517 4518 4519 4520 4521 4522 4523 4524
## [4525] 4525 4526 4527 4528 4529 4530 4531 4532 4533 4534 4535 4536
## [4537] 4537 4538 4539 4540 4541 4542 4543 4544 4545 4546 4547 4548
## [4549] 4549 4550 4551 4552 4553 4554 4555 4556 4557 4558 4559 4560
## [4561] 4561 4562 4563 4564 4565 4566 4567 4568 4569 4570 4571 4572
## [4573] 4573 4574 4575 4576 4577 4578 4579 4580 4581 4582 4583 4584
## [4585] 4585 4586 4587 4588 4589 4590 4591 4592 4593 4594 4595 4596
## [4597] 4597 4598 4599 4600 4601 4602 4603 4604 4605 4606 4607 4608
## [4609] 4609 4610 4611 4612 4613 4614 4615 4616 4617 4618 4619 4620
## [4621] 4621 4622 4623 4624 4625 4626 4627 4628 4629 4630 4631 4632
## [4633] 4633 4634 4635 4636 4637 4638 4639 4640 4641 4642 4643 4644
## [4645] 4645 4646 4647 4648 4649 4650 4651 4652 4653 4654 4655 4656
## [4657] 4657 4658 4659 4660 4661 4662 4663 4664 4665 4666 4667 4668
## [4669] 4669 4670 4671 4672 4673 4674 4675 4676 4677 4678 4679 4680
## [4681] 4681 4682 4683 4684 4685 4686 4687 4688 4689 4690 4691 4692
## [4693] 4693 4694 4695 4696 4697 4698 4699 4700 4701 4702 4703 4704
## [4705] 4705 4706 4707 4708 4709 4710 4711 4712 4713 4714 4715 4716
## [4717] 4717 4718 4719 4720 4721 4722 4723 4724 4725 4726 4727 4728
## [4729] 4729 4730 4731 4732 4733 4734 4735 4736 4737 4738 4739 4740
## [4741] 4741 4742 4743 4744 4745 4746 4747 4748 4749 4750 4751 4752
## [4753] 4753 4754 4755 4756 4757 4758 4759 4760 4761 4762 4763 4764
## [4765] 4765 4766 4767 4768 4769 4770 4771 4772 4773 4774 4775 4776
## [4777] 4777 4778 4779 4780 4781 4782 4783 4784 4785 4786 4787 4788
## [4789] 4789 4790 4791 4792 4793 4794 4795 4796 4797 4798 4799 4800
## [4801] 4801 4802 4803 4804 4805 4806 4807 4808 4809 4810 4811 4812
## [4813] 4813 4814 4815 4816 4817 4818 4819 4820 4821 4822 4823 4824
## [4825] 4825 4826 4827 4828 4829 4830 4831 4832 4833 4834 4835 4836
## [4837] 4837 4838 4839 4840 4841 4842 4843 4844 4845 4846 4847 4848
## [4849] 4849 4850 4851 4852 4853 4854 4855 4856 4857 4858 4859 4860
## [4861] 4861 4862 4863 4864 4865 4866 4867 4868 4869 4870 4871 4872
## [4873] 4873 4874 4875 4876 4877 4878 4879 4880 4881 4882 4883 4884
## [4885] 4885 4886 4887 4888 4889 4890 4891 4892 4893 4894 4895 4896
## [4897] 4897 4898 4899 4900 4901 4902 4903 4904 4905 4906 4907 4908
## [4909] 4909 4910 4911 4912 4913 4914 4915 4916 4917 4918 4919 4920
## [4921] 4921 4922 4923 4924 4925 4926 4927 4928 4929 4930 4931 4932
## [4933] 4933 4934 4935 4936 4937 4938 4939 4940 4941 4942 4943 4944
## [4945] 4945 4946 4947 4948 4949 4950 4951 4952 4953 4954 4955 4956
## [4957] 4957 4958 4959 4960 4961 4962 4963 4964 4965 4966 4967 4968
## [4969] 4969 4970 4971 4972 4973 4974 4975 4976 4977 4978 4979 4980
## [4981] 4981 4982 4983 4984 4985 4986 4987 4988 4989 4990 4991 4992
## [4993] 4993 4994 4995 4996 4997 4998 4999 5000 5001 5002 5003 5004
## [5005] 5005 5006 5007 5008 5009 5010 5011 5012 5013 5014 5015 5016
## [5017] 5017 5018 5019 5020 5021 5022 5023 5024 5025 5026 5027 5028
## [5029] 5029 5030 5031 5032 5033 5034 5035 5036 5037 5038 5039 5040
## [5041] 5041 5042 5043 5044 5045 5046 5047 5048 5049 5050 5051 5052
## [5053] 5053 5054 5055 5056 5057 5058 5059 5060 5061 5062 5063 5064
## [5065] 5065 5066 5067 5068 5069 5070 5071 5072 5073 5074 5075 5076
## [5077] 5077 5078 5079 5080 5081 5082 5083 5084 5085 5086 5087 5088
## [5089] 5089 5090 5091 5092 5093 5094 5095 5096 5097 5098 5099 5100
## [5101] 5101 5102 5103 5104 5105 5106 5107 5108 5109 5110 5111 5112
## [5113] 5113 5114 5115 5116 5117 5118 5119 5120 5121 5122 5123 5124
## [5125] 5125 5126 5127 5128 5129 5130 5131 5132 5133 5134 5135 5136
## [5137] 5137 5138 5139 5140 5141 5142 5143 5144 5145 5146 5147 5148
## [5149] 5149 5150 5151 5152 5153 5154 5155 5156 5157 5158 5159 5160
## [5161] 5161 5162 5163 5164 5165 5166 5167 5168 5169 5170 5171 5172
## [5173] 5173 5174 5175 5176 5177 5178 5179 5180 5181 5182 5183 5184
## [5185] 5185 5186 5187 5188 5189 5190 5191 5192 5193 5194 5195 5196
## [5197] 5197 5198 5199 5200 5201 5202 5203 5204 5205 5206 5207 5208
## [5209] 5209 5210 5211 5212 5213 5214 5215 5216 5217 5218 5219 5220
## [5221] 5221 5222 5223 5224 5225 5226 5227 5228 5229 5230 5231 5232
## [5233] 5233 5234 5235 5236 5237 5238 5239 5240 5241 5242 5243 5244
## [5245] 5245 5246 5247 5248 5249 5250 5251 5252 5253 5254 5255 5256
## [5257] 5257 5258 5259 5260 5261 5262 5263 5264 5265 5266 5267 5268
## [5269] 5269 5270 5271 5272 5273 5274 5275 5276 5277 5278 5279 5280
## [5281] 5281 5282 5283 5284 5285 5286 5287 5288 5289 5290 5291 5292
## [5293] 5293 5294 5295 5296 5297 5298 5299 5300 5301 5302 5303 5304
## [5305] 5305 5306 5307 5308 5309 5310 5311 5312 5313 5314 5315 5316
## [5317] 5317 5318 5319 5320 5321 5322 5323 5324 5325 5326 5327 5328
## [5329] 5329 5330 5331 5332 5333 5334 5335 5336 5337 5338 5339 5340
## [5341] 5341 5342 5343 5344 5345 5346 5347 5348 5349 5350 5351 5352
## [5353] 5353 5354 5355 5356 5357 5358 5359 5360 5361 5362 5363 5364
## [5365] 5365 5366 5367 5368 5369 5370 5371 5372 5373 5374 5375 5376
## [5377] 5377 5378 5379 5380 5381 5382 5383 5384 5385 5386 5387 5388
## [5389] 5389 5390 5391 5392 5393 5394 5395 5396 5397 5398 5399 5400
## [5401] 5401 5402 5403 5404 5405 5406 5407 5408 5409 5410 5411 5412
## [5413] 5413 5414 5415 5416 5417 5418 5419 5420 5421 5422 5423 5424
## [5425] 5425 5426 5427 5428 5429 5430 5431 5432 5433 5434 5435 5436
## [5437] 5437 5438 5439 5440 5441 5442 5443 5444 5445 5446 5447 5448
## [5449] 5449 5450 5451 5452 5453 5454 5455 5456 5457 5458 5459 5460
## [5461] 5461 5462 5463 5464 5465 5466 5467 5468 5469 5470 5471 5472
## [5473] 5473 5474 5475 5476 5477 5478 5479 5480 5481 5482 5483 5484
## [5485] 5485 5486 5487 5488 5489 5490 5491 5492 5493 5494 5495 5496
## [5497] 5497 5498 5499 5500 5501 5502 5503 5504 5505 5506 5507 5508
## [5509] 5509 5510 5511 5512 5513 5514 5515 5516 5517 5518 5519 5520
## [5521] 5521 5522 5523 5524 5525 5526 5527 5528 5529 5530 5531 5532
## [5533] 5533 5534 5535 5536 5537 5538 5539 5540 5541 5542 5543 5544
## [5545] 5545 5546 5547 5548 5549 5550 5551 5552 5553 5554 5555 5556
## [5557] 5557 5558 5559 5560 5561 5562 5563 5564 5565 5566 5567 5568
## [5569] 5569 5570 5571 5572 5573 5574 5575 5576 5577 5578 5579 5580
## [5581] 5581 5582 5583 5584 5585 5586 5587 5588 5589 5590 5591 5592
## [5593] 5593 5594 5595 5596 5597 5598 5599 5600 5601 5602 5603 5604
## [5605] 5605 5606 5607 5608 5609 5610 5611 5612 5613 5614 5615 5616
## [5617] 5617 5618 5619 5620 5621 5622 5623 5624 5625 5626 5627 5628
## [5629] 5629 5630 5631 5632 5633 5634 5635 5636 5637 5638 5639 5640
## [5641] 5641 5642 5643 5644 5645 5646 5647 5648 5649 5650 5651 5652
## [5653] 5653 5654 5655 5656 5657 5658 5659 5660 5661 5662 5663 5664
## [5665] 5665 5666 5667 5668 5669 5670 5671 5672 5673 5674 5675 5676
## [5677] 5677 5678 5679 5680 5681 5682 5683 5684 5685 5686 5687 5688
## [5689] 5689 5690 5691 5692 5693 5694 5695 5696 5697 5698 5699 5700
## [5701] 5701 5702 5703 5704 5705 5706 5707 5708 5709 5710 5711 5712
## [5713] 5713 5714 5715 5716 5717 5718 5719 5720 5721 5722 5723 5724
## [5725] 5725 5726 5727 5728 5729 5730 5731 5732 5733 5734 5735 5736
## [5737] 5737 5738 5739 5740 5741 5742 5743 5744 5745 5746 5747 5748
## [5749] 5749 5750 5751 5752 5753 5754 5755 5756 5757 5758 5759 5760
## [5761] 5761 5762 5763 5764 5765 5766 5767 5768 5769 5770 5771 5772
## [5773] 5773 5774 5775 5776 5777 5778 5779 5780 5781 5782 5783 5784
## [5785] 5785 5786 5787 5788 5789 5790 5791 5792 5793 5794 5795 5796
## [5797] 5797 5798 5799 5800 5801 5802 5803 5804 5805 5806 5807 5808
## [5809] 5809 5810 5811 5812 5813 5814 5815 5816 5817 5818 5819 5820
## [5821] 5821 5822 5823 5824 5825 5826 5827 5828 5829 5830 5831 5832
## [5833] 5833 5834 5835 5836 5837 5838 5839 5840 5841 5842 5843 5844
## [5845] 5845 5846 5847 5848 5849 5850 5851 5852 5853 5854 5855 5856
## [5857] 5857 5858 5859 5860 5861 5862 5863 5864 5865 5866 5867 5868
## [5869] 5869 5870 5871 5872 5873 5874 5875 5876 5877 5878 5879 5880
## [5881] 5881 5882 5883 5884 5885 5886 5887 5888 5889 5890 5891 5892
## [5893] 5893 5894 5895 5896 5897 5898 5899 5900 5901 5902 5903 5904
## [5905] 5905 5906 5907 5908 5909 5910 5911 5912 5913 5914 5915 5916
## [5917] 5917 5918 5919 5920 5921 5922 5923 5924 5925 5926 5927 5928
## [5929] 5929 5930 5931 5932 5933 5934 5935 5936 5937 5938 5939 5940
## [5941] 5941 5942 5943 5944 5945 5946 5947 5948 5949 5950 5951 5952
## [5953] 5953 5954 5955 5956 5957 5958 5959 5960 5961 5962 5963 5964
## [5965] 5965 5966 5967 5968 5969 5970 5971 5972 5973 5974 5975 5976
## [5977] 5977 5978 5979 5980 5981 5982 5983 5984 5985 5986 5987 5988
## [5989] 5989 5990 5991 5992 5993 5994 5995 5996 5997 5998 5999 6000
## [6001] 6001 6002 6003 6004 6005 6006 6007 6008 6009 6010 6011 6012
## [6013] 6013 6014 6015 6016 6017 6018 6019 6020 6021 6022 6023 6024
## [6025] 6025 6026 6027 6028 6029 6030 6031 6032 6033 6034 6035 6036
## [6037] 6037 6038 6039 6040 6041 6042 6043 6044 6045 6046 6047 6048
## [6049] 6049 6050 6051 6052 6053 6054 6055 6056 6057 6058 6059 6060
## [6061] 6061 6062 6063 6064 6065 6066 6067 6068 6069 6070 6071 6072
## [6073] 6073 6074 6075 6076 6077 6078 6079 6080 6081 6082 6083 6084
## [6085] 6085 6086 6087 6088 6089 6090 6091 6092 6093 6094 6095 6096
## [6097] 6097 6098 6099 6100 6101 6102 6103 6104 6105 6106 6107 6108
## [6109] 6109 6110 6111 6112 6113 6114 6115 6116 6117 6118 6119 6120
## [6121] 6121 6122 6123 6124 6125 6126 6127 6128 6129 6130 6131 6132
## [6133] 6133 6134 6135 6136 6137 6138 6139 6140 6141 6142 6143 6144
## [6145] 6145 6146 6147 6148 6149 6150 6151 6152 6153 6154 6155 6156
## [6157] 6157 6158 6159 6160 6161 6162 6163 6164 6165 6166 6167 6168
## [6169] 6169 6170 6171 6172 6173 6174 6175 6176 6177 6178 6179 6180
## [6181] 6181 6182 6183 6184 6185 6186 6187 6188 6189 6190 6191 6192
## [6193] 6193 6194 6195 6196 6197 6198 6199 6200 6201 6202 6203 6204
## [6205] 6205 6206 6207 6208 6209 6210 6211 6212 6213 6214 6215 6216
## [6217] 6217 6218 6219 6220 6221 6222 6223 6224 6225 6226 6227 6228
## [6229] 6229 6230 6231 6232 6233 6234 6235 6236 6237 6238 6239 6240
## [6241] 6241 6242 6243 6244 6245 6246 6247 6248 6249 6250 6251 6252
## [6253] 6253 6254 6255 6256 6257 6258 6259 6260 6261 6262 6263 6264
## [6265] 6265 6266 6267 6268 6269 6270 6271 6272 6273 6274 6275 6276
## [6277] 6277 6278 6279 6280 6281 6282 6283 6284 6285 6286 6287 6288
## [6289] 6289 6290 6291 6292 6293 6294 6295 6296 6297 6298 6299 6300
## [6301] 6301 6302 6303 6304 6305 6306 6307 6308 6309 6310 6311 6312
## [6313] 6313 6314 6315 6316 6317 6318 6319 6320 6321 6322 6323 6324
## [6325] 6325 6326 6327 6328 6329 6330 6331 6332 6333 6334 6335 6336
## [6337] 6337 6338 6339 6340 6341 6342 6343 6344 6345 6346 6347 6348
## [6349] 6349 6350 6351 6352 6353 6354 6355 6356 6357 6358 6359 6360
## [6361] 6361 6362 6363 6364 6365 6366 6367 6368 6369 6370 6371 6372
## [6373] 6373 6374 6375 6376 6377 6378 6379 6380 6381 6382 6383 6384
## [6385] 6385 6386 6387 6388 6389 6390 6391 6392 6393 6394 6395 6396
## [6397] 6397 6398 6399 6400 6401 6402 6403 6404 6405 6406 6407 6408
## [6409] 6409 6410 6411 6412 6413 6414 6415 6416 6417 6418 6419 6420
## [6421] 6421 6422 6423 6424 6425 6426 6427 6428 6429 6430 6431 6432
## [6433] 6433 6434 6435 6436 6437 6438 6439 6440 6441 6442 6443 6444
## [6445] 6445 6446 6447 6448 6449 6450 6451 6452 6453 6454 6455 6456
## [6457] 6457 6458 6459 6460 6461 6462 6463 6464 6465 6466 6467 6468
## [6469] 6469 6470 6471 6472 6473 6474 6475 6476 6477 6478 6479 6480
## [6481] 6481 6482 6483 6484 6485 6486 6487 6488 6489 6490 6491 6492
## [6493] 6493 6494 6495 6496 6497 6498 6499 6500 6501 6502 6503 6504
## [6505] 6505 6506 6507 6508 6509 6510 6511 6512 6513 6514 6515 6516
## [6517] 6517 6518 6519 6520 6521 6522 6523 6524 6525 6526 6527 6528
## [6529] 6529 6530 6531 6532 6533 6534 6535 6536 6537 6538 6539 6540
## [6541] 6541 6542 6543 6544 6545 6546 6547 6548 6549 6550 6551 6552
## [6553] 6553 6554 6555 6556 6557 6558 6559 6560 6561 6562 6563 6564
## [6565] 6565 6566 6567 6568 6569 6570 6571 6572 6573 6574 6575 6576
## [6577] 6577 6578 6579 6580 6581 6582 6583 6584 6585 6586 6587 6588
## [6589] 6589 6590 6591 6592 6593 6594 6595 6596 6597 6598 6599 6600
## [6601] 6601 6602 6603 6604 6605 6606 6607 6608 6609 6610 6611 6612
## [6613] 6613 6614 6615 6616 6617 6618 6619 6620 6621 6622 6623 6624
## [6625] 6625 6626 6627 6628 6629 6630 6631 6632 6633 6634 6635 6636
## [6637] 6637 6638 6639 6640 6641 6642 6643 6644 6645 6646 6647 6648
## [6649] 6649 6650 6651 6652 6653 6654 6655 6656 6657 6658 6659 6660
## [6661] 6661 6662 6663 6664 6665 6666 6667 6668 6669 6670 6671 6672
## [6673] 6673 6674 6675 6676 6677 6678 6679 6680 6681 6682 6683 6684
## [6685] 6685 6686 6687 6688 6689 6690 6691 6692 6693 6694 6695 6696
## [6697] 6697 6698 6699 6700 6701 6702 6703 6704 6705 6706 6707 6708
## [6709] 6709 6710 6711 6712 6713 6714 6715 6716 6717 6718 6719 6720
## [6721] 6721 6722 6723 6724 6725 6726 6727 6728 6729 6730 6731 6732
## [6733] 6733 6734 6735 6736 6737 6738 6739 6740 6741 6742 6743 6744
## [6745] 6745 6746 6747 6748 6749 6750 6751 6752 6753 6754 6755 6756
## [6757] 6757 6758 6759 6760 6761 6762 6763 6764 6765 6766 6767 6768
## [6769] 6769 6770 6771 6772 6773 6774 6775 6776 6777 6778 6779 6780
## [6781] 6781 6782 6783 6784 6785 6786 6787 6788 6789 6790 6791 6792
## [6793] 6793 6794 6795 6796 6797 6798 6799 6800 6801 6802 6803 6804
## [6805] 6805 6806 6807 6808 6809 6810 6811 6812 6813 6814 6815 6816
## [6817] 6817 6818 6819 6820 6821 6822 6823 6824 6825 6826 6827 6828
## [6829] 6829 6830 6831 6832 6833 6834 6835 6836 6837 6838 6839 6840
## [6841] 6841 6842 6843 6844 6845 6846 6847 6848 6849 6850 6851 6852
## [6853] 6853 6854 6855 6856 6857 6858 6859 6860 6861 6862 6863 6864
## [6865] 6865 6866 6867 6868 6869 6870 6871 6872 6873 6874 6875 6876
## [6877] 6877 6878 6879 6880 6881 6882 6883 6884 6885 6886 6887 6888
## [6889] 6889 6890 6891 6892 6893 6894 6895 6896 6897 6898 6899 6900
## [6901] 6901 6902 6903 6904 6905 6906 6907 6908 6909 6910 6911 6912
## [6913] 6913 6914 6915 6916 6917 6918 6919 6920 6921 6922 6923 6924
## [6925] 6925 6926 6927 6928 6929 6930 6931 6932 6933 6934 6935 6936
## [6937] 6937 6938 6939 6940 6941 6942 6943 6944 6945 6946 6947 6948
## [6949] 6949 6950 6951 6952 6953 6954 6955 6956 6957 6958 6959 6960
## [6961] 6961 6962 6963 6964 6965 6966 6967 6968 6969 6970 6971 6972
## [6973] 6973 6974 6975 6976 6977 6978 6979 6980 6981 6982 6983 6984
## [6985] 6985 6986 6987 6988 6989 6990 6991 6992 6993 6994 6995 6996
## [6997] 6997 6998 6999 7000 7001 7002 7003 7004 7005 7006 7007 7008
## [7009] 7009 7010 7011 7012 7013 7014 7015 7016 7017 7018 7019 7020
## [7021] 7021 7022 7023 7024 7025 7026 7027 7028 7029 7030 7031 7032
## [7033] 7033 7034 7035 7036 7037 7038 7039 7040 7041 7042 7043 7044
## [7045] 7045 7046 7047 7048 7049 7050 7051 7052 7053 7054 7055 7056
## [7057] 7057 7058 7059 7060 7061 7062 7063 7064 7065 7066 7067 7068
## [7069] 7069 7070 7071 7072 7073 7074 7075 7076 7077 7078 7079 7080
## [7081] 7081 7082 7083 7084 7085 7086 7087 7088 7089 7090 7091 7092
## [7093] 7093 7094 7095 7096 7097 7098 7099 7100 7101 7102 7103 7104
## [7105] 7105 7106 7107 7108 7109 7110 7111 7112 7113 7114 7115 7116
## [7117] 7117 7118 7119 7120 7121 7122 7123 7124 7125 7126 7127 7128
## [7129] 7129 7130 7131 7132 7133 7134 7135 7136 7137 7138 7139 7140
## [7141] 7141 7142 7143 7144 7145 7146 7147 7148 7149 7150 7151 7152
## [7153] 7153 7154 7155 7156 7157 7158 7159 7160 7161 7162 7163 7164
## [7165] 7165 7166 7167 7168 7169 7170 7171 7172 7173 7174 7175 7176
## [7177] 7177 7178 7179 7180 7181 7182 7183 7184 7185 7186 7187 7188
## [7189] 7189 7190 7191 7192 7193 7194 7195 7196 7197 7198 7199 7200
## [7201] 7201 7202 7203 7204 7205 7206 7207 7208 7209 7210 7211 7212
## [7213] 7213 7214 7215 7216 7217 7218 7219 7220 7221 7222 7223 7224
## [7225] 7225 7226 7227 7228 7229 7230 7231 7232 7233 7234 7235 7236
## [7237] 7237 7238 7239 7240 7241 7242 7243 7244 7245 7246 7247 7248
## [7249] 7249 7250 7251 7252 7253 7254 7255 7256 7257 7258 7259 7260
## [7261] 7261 7262 7263 7264 7265 7266 7267 7268 7269 7270 7271 7272
## [7273] 7273 7274 7275 7276 7277 7278 7279 7280 7281 7282 7283 7284
## [7285] 7285 7286 7287 7288 7289 7290 7291 7292 7293 7294 7295 7296
## [7297] 7297 7298 7299 7300 7301 7302 7303 7304 7305 7306 7307 7308
## [7309] 7309 7310 7311 7312 7313 7314 7315 7316 7317 7318 7319 7320
## [7321] 7321 7322 7323 7324 7325 7326 7327 7328 7329 7330 7331 7332
## [7333] 7333 7334 7335 7336 7337 7338 7339 7340 7341 7342 7343 7344
## [7345] 7345 7346 7347 7348 7349 7350 7351 7352 7353 7354 7355 7356
## [7357] 7357 7358 7359 7360 7361 7362 7363 7364 7365 7366 7367 7368
## [7369] 7369 7370 7371 7372 7373 7374 7375 7376 7377 7378 7379 7380
## [7381] 7381 7382 7383 7384 7385 7386 7387 7388 7389 7390 7391 7392
## [7393] 7393 7394 7395 7396 7397 7398 7399 7400 7401 7402 7403 7404
## [7405] 7405 7406 7407 7408 7409 7410 7411 7412 7413 7414 7415 7416
## [7417] 7417 7418 7419 7420 7421 7422 7423 7424 7425 7426 7427 7428
## [7429] 7429 7430 7431 7432 7433 7434 7435 7436 7437 7438 7439 7440
## [7441] 7441 7442 7443 7444 7445 7446 7447 7448 7449 7450 7451 7452
## [7453] 7453 7454 7455 7456 7457 7458 7459 7460 7461 7462 7463 7464
## [7465] 7465 7466 7467 7468 7469 7470 7471 7472 7473 7474 7475 7476
## [7477] 7477 7478 7479 7480 7481 7482 7483 7484 7485 7486 7487 7488
## [7489] 7489 7490 7491 7492 7493 7494 7495 7496 7497 7498 7499 7500
## [7501] 7501 7502 7503 7504 7505 7506 7507 7508 7509 7510 7511 7512
## [7513] 7513 7514 7515 7516 7517 7518 7519 7520 7521 7522 7523 7524
## [7525] 7525 7526 7527 7528 7529 7530 7531 7532 7533 7534 7535 7536
## [7537] 7537 7538 7539 7540 7541 7542 7543 7544 7545 7546 7547 7548
## [7549] 7549 7550 7551 7552 7553 7554 7555 7556 7557 7558 7559 7560
## [7561] 7561 7562 7563 7564 7565 7566 7567 7568 7569 7570 7571 7572
## [7573] 7573 7574 7575 7576 7577 7578 7579 7580 7581 7582 7583 7584
## [7585] 7585 7586 7587 7588 7589 7590 7591 7592 7593 7594 7595 7596
## [7597] 7597 7598 7599 7600 7601 7602 7603 7604 7605 7606 7607 7608
## [7609] 7609 7610 7611 7612 7613 7614 7615 7616 7617 7618 7619 7620
## [7621] 7621 7622 7623 7624 7625 7626 7627 7628 7629 7630 7631 7632
## [7633] 7633 7634 7635 7636 7637 7638 7639 7640 7641 7642 7643 7644
## [7645] 7645 7646 7647 7648 7649 7650 7651 7652 7653 7654 7655 7656
## [7657] 7657 7658 7659 7660 7661 7662 7663 7664 7665 7666 7667 7668
## [7669] 7669 7670 7671 7672 7673 7674 7675 7676 7677 7678 7679 7680
## [7681] 7681 7682 7683 7684 7685 7686 7687 7688 7689 7690 7691 7692
## [7693] 7693 7694 7695 7696 7697 7698 7699 7700 7701 7702 7703 7704
## [7705] 7705 7706 7707 7708 7709 7710 7711 7712 7713 7714 7715 7716
## [7717] 7717 7718 7719 7720 7721 7722 7723 7724 7725 7726 7727 7728
## [7729] 7729 7730 7731 7732 7733 7734 7735 7736 7737 7738 7739 7740
## [7741] 7741 7742 7743 7744 7745 7746 7747 7748 7749 7750 7751 7752
## [7753] 7753 7754 7755 7756 7757 7758 7759 7760 7761 7762 7763 7764
## [7765] 7765 7766 7767 7768 7769 7770 7771 7772 7773 7774 7775 7776
## [7777] 7777 7778 7779 7780 7781 7782 7783 7784 7785 7786 7787 7788
## [7789] 7789 7790 7791 7792 7793 7794 7795 7796 7797 7798 7799 7800
## [7801] 7801 7802 7803 7804 7805 7806 7807 7808 7809 7810 7811 7812
## [7813] 7813 7814 7815 7816 7817 7818 7819 7820 7821 7822 7823 7824
## [7825] 7825 7826 7827 7828 7829 7830 7831 7832 7833 7834 7835 7836
## [7837] 7837 7838 7839 7840 7841 7842 7843 7844 7845 7846 7847 7848
## [7849] 7849 7850 7851 7852 7853 7854 7855 7856 7857 7858 7859 7860
## [7861] 7861 7862 7863 7864 7865 7866 7867 7868 7869 7870 7871 7872
## [7873] 7873 7874 7875 7876 7877 7878 7879 7880 7881 7882 7883 7884
## [7885] 7885 7886 7887 7888 7889 7890 7891 7892 7893 7894 7895 7896
## [7897] 7897 7898 7899 7900 7901 7902 7903 7904 7905 7906 7907 7908
## [7909] 7909 7910 7911 7912 7913 7914 7915 7916 7917 7918 7919 7920
## [7921] 7921 7922 7923 7924 7925 7926 7927 7928 7929 7930 7931 7932
## [7933] 7933 7934 7935 7936 7937 7938 7939 7940 7941 7942 7943 7944
## [7945] 7945 7946 7947 7948 7949 7950 7951 7952 7953 7954 7955 7956
## [7957] 7957 7958 7959 7960 7961 7962 7963 7964 7965 7966 7967 7968
## [7969] 7969 7970 7971 7972 7973 7974 7975 7976 7977 7978 7979 7980
## [7981] 7981 7982 7983 7984 7985 7986 7987 7988 7989 7990 7991 7992
## [7993] 7993 7994 7995 7996 7997 7998 7999 8000 8001 8002 8003 8004
## [8005] 8005 8006 8007 8008 8009 8010 8011 8012 8013 8014 8015 8016
## [8017] 8017 8018 8019 8020 8021 8022 8023 8024 8025 8026 8027 8028
## [8029] 8029 8030 8031 8032 8033 8034 8035 8036 8037 8038 8039 8040
## [8041] 8041 8042 8043 8044 8045 8046 8047 8048 8049 8050 8051 8052
## [8053] 8053 8054 8055 8056 8057 8058 8059 8060 8061 8062 8063 8064
## [8065] 8065 8066 8067 8068 8069 8070 8071 8072 8073 8074 8075 8076
## [8077] 8077 8078 8079 8080 8081 8082 8083 8084 8085 8086 8087 8088
## [8089] 8089 8090 8091 8092 8093 8094 8095 8096 8097 8098 8099 8100
## [8101] 8101 8102 8103 8104 8105 8106 8107 8108 8109 8110 8111 8112
## [8113] 8113 8114 8115 8116 8117 8118 8119 8120 8121 8122 8123 8124
## [8125] 8125 8126 8127 8128 8129 8130 8131 8132 8133 8134 8135 8136
## [8137] 8137 8138 8139 8140 8141 8142 8143 8144 8145 8146 8147 8148
## [8149] 8149 8150 8151 8152 8153 8154 8155 8156 8157 8158 8159 8160
## [8161] 8161 8162 8163 8164 8165 8166 8167 8168 8169 8170 8171 8172
## [8173] 8173 8174 8175 8176 8177 8178 8179 8180 8181 8182 8183 8184
## [8185] 8185 8186 8187 8188 8189 8190 8191 8192 8193 8194 8195 8196
## [8197] 8197 8198 8199 8200 8201 8202 8203 8204 8205 8206 8207 8208
## [8209] 8209 8210 8211 8212 8213 8214 8215 8216 8217 8218 8219 8220
## [8221] 8221 8222 8223 8224 8225 8226 8227 8228 8229 8230 8231 8232
## [8233] 8233 8234 8235 8236 8237 8238 8239 8240 8241 8242 8243 8244
## [8245] 8245 8246 8247 8248 8249 8250 8251 8252 8253 8254 8255 8256
## [8257] 8257 8258 8259 8260 8261 8262 8263 8264 8265 8266 8267 8268
## [8269] 8269 8270 8271 8272 8273 8274 8275 8276 8277 8278 8279 8280
## [8281] 8281 8282 8283 8284 8285 8286 8287 8288 8289 8290 8291 8292
## [8293] 8293 8294 8295 8296 8297 8298 8299 8300 8301 8302 8303 8304
## [8305] 8305 8306 8307 8308 8309 8310 8311 8312 8313 8314 8315 8316
## [8317] 8317 8318 8319 8320 8321 8322 8323 8324 8325 8326 8327 8328
## [8329] 8329 8330 8331 8332 8333 8334 8335 8336 8337 8338 8339 8340
## [8341] 8341 8342 8343 8344 8345 8346 8347 8348 8349 8350 8351 8352
## [8353] 8353 8354 8355 8356 8357 8358 8359 8360 8361 8362 8363 8364
## [8365] 8365 8366 8367 8368 8369 8370 8371 8372 8373 8374 8375 8376
## [8377] 8377 8378 8379 8380 8381 8382 8383 8384 8385 8386 8387 8388
## [8389] 8389 8390 8391 8392 8393 8394 8395 8396 8397 8398 8399 8400
## [8401] 8401 8402 8403 8404 8405 8406 8407 8408 8409 8410 8411 8412
## [8413] 8413 8414 8415 8416 8417 8418 8419 8420 8421 8422 8423 8424
## [8425] 8425 8426 8427 8428 8429 8430 8431 8432 8433 8434 8435 8436
## [8437] 8437 8438 8439 8440 8441 8442 8443 8444 8445 8446 8447 8448
## [8449] 8449 8450 8451 8452 8453 8454 8455 8456 8457 8458 8459 8460
## [8461] 8461 8462 8463 8464 8465 8466 8467 8468 8469 8470 8471 8472
## [8473] 8473 8474 8475 8476 8477 8478 8479 8480 8481 8482 8483 8484
## [8485] 8485 8486 8487 8488 8489 8490 8491 8492 8493 8494 8495 8496
## [8497] 8497 8498 8499 8500 8501 8502 8503 8504 8505 8506 8507 8508
## [8509] 8509 8510 8511 8512 8513 8514 8515 8516 8517 8518 8519 8520
## [8521] 8521 8522 8523 8524 8525 8526 8527 8528 8529 8530 8531 8532
## [8533] 8533 8534 8535 8536 8537 8538 8539 8540 8541 8542 8543 8544
## [8545] 8545 8546 8547 8548 8549 8550 8551 8552 8553 8554 8555 8556
## [8557] 8557 8558 8559 8560 8561 8562 8563 8564 8565 8566 8567 8568
## [8569] 8569 8570 8571 8572 8573 8574 8575 8576 8577 8578 8579 8580
## [8581] 8581 8582 8583 8584 8585 8586 8587 8588 8589 8590 8591 8592
## [8593] 8593 8594 8595 8596 8597 8598 8599 8600 8601 8602 8603 8604
## [8605] 8605 8606 8607 8608 8609 8610 8611 8612 8613 8614 8615 8616
## [8617] 8617 8618 8619 8620 8621 8622 8623 8624 8625 8626 8627 8628
## [8629] 8629 8630 8631 8632 8633 8634 8635 8636 8637 8638 8639 8640
## [8641] 8641 8642 8643 8644 8645 8646 8647 8648 8649 8650 8651 8652
## [8653] 8653 8654 8655 8656 8657 8658 8659 8660 8661 8662 8663 8664
## [8665] 8665 8666 8667 8668 8669 8670 8671 8672 8673 8674 8675 8676
## [8677] 8677 8678 8679 8680 8681 8682 8683 8684 8685 8686 8687 8688
## [8689] 8689 8690 8691 8692 8693 8694 8695 8696 8697 8698 8699 8700
## [8701] 8701 8702 8703 8704 8705 8706 8707 8708 8709 8710 8711 8712
## [8713] 8713 8714 8715 8716 8717 8718 8719 8720 8721 8722 8723 8724
## [8725] 8725 8726 8727 8728 8729 8730 8731 8732 8733 8734 8735 8736
## [8737] 8737 8738 8739 8740 8741 8742 8743 8744 8745 8746 8747 8748
## [8749] 8749 8750 8751 8752 8753 8754 8755 8756 8757 8758 8759 8760
## [8761] 8761 8762 8763 8764 8765 8766 8767 8768 8769 8770 8771 8772
## [8773] 8773 8774 8775 8776 8777 8778 8779 8780 8781 8782 8783 8784
## [8785] 8785 8786 8787 8788 8789 8790 8791 8792 8793 8794 8795 8796
## [8797] 8797 8798 8799 8800 8801 8802 8803 8804 8805 8806 8807 8808
## [8809] 8809 8810 8811 8812 8813 8814 8815 8816 8817 8818 8819 8820
## [8821] 8821 8822 8823 8824 8825 8826 8827 8828 8829 8830 8831 8832
## [8833] 8833 8834 8835 8836 8837 8838 8839 8840 8841 8842 8843 8844
## [8845] 8845 8846 8847 8848 8849 8850 8851 8852 8853 8854 8855 8856
## [8857] 8857 8858 8859 8860 8861 8862 8863 8864 8865 8866 8867 8868
## [8869] 8869 8870 8871 8872 8873 8874 8875 8876 8877 8878 8879 8880
## [8881] 8881 8882 8883 8884 8885 8886 8887 8888 8889 8890 8891 8892
## [8893] 8893 8894 8895 8896 8897 8898 8899 8900 8901 8902 8903 8904
## [8905] 8905 8906 8907 8908 8909 8910 8911 8912 8913 8914 8915 8916
## [8917] 8917 8918 8919 8920 8921 8922 8923 8924 8925 8926 8927 8928
## [8929] 8929 8930 8931 8932 8933 8934 8935 8936 8937 8938 8939 8940
## [8941] 8941 8942 8943 8944 8945 8946 8947 8948 8949 8950 8951 8952
## [8953] 8953 8954 8955 8956 8957 8958 8959 8960 8961 8962 8963 8964
## [8965] 8965 8966 8967 8968 8969 8970 8971 8972 8973 8974 8975 8976
## [8977] 8977 8978 8979 8980 8981 8982 8983 8984 8985 8986 8987 8988
## [8989] 8989 8990 8991 8992 8993 8994 8995 8996 8997 8998 8999 9000
## [9001] 9001 9002 9003 9004 9005 9006 9007 9008 9009 9010 9011 9012
## [9013] 9013 9014 9015 9016 9017 9018 9019 9020 9021 9022 9023 9024
## [9025] 9025 9026 9027 9028 9029 9030 9031 9032 9033 9034 9035 9036
## [9037] 9037 9038 9039 9040 9041 9042 9043 9044 9045 9046 9047 9048
## [9049] 9049 9050 9051 9052 9053 9054 9055 9056 9057 9058 9059 9060
## [9061] 9061 9062 9063 9064 9065 9066 9067 9068 9069 9070 9071 9072
## [9073] 9073 9074 9075 9076 9077 9078 9079 9080 9081 9082 9083 9084
## [9085] 9085 9086 9087 9088 9089 9090 9091 9092 9093 9094 9095 9096
## [9097] 9097 9098 9099 9100 9101 9102 9103 9104 9105 9106 9107 9108
## [9109] 9109 9110 9111 9112 9113 9114 9115 9116 9117 9118 9119 9120
## [9121] 9121 9122 9123 9124 9125 9126 9127 9128 9129 9130 9131 9132
## [9133] 9133 9134 9135 9136 9137 9138 9139 9140 9141 9142 9143 9144
## [9145] 9145 9146 9147 9148 9149 9150 9151 9152 9153 9154 9155 9156
## [9157] 9157 9158 9159 9160 9161 9162 9163 9164 9165 9166 9167 9168
## [9169] 9169 9170 9171 9172 9173 9174 9175 9176 9177 9178 9179 9180
## [9181] 9181 9182 9183 9184 9185 9186 9187 9188 9189 9190 9191 9192
## [9193] 9193 9194 9195 9196 9197 9198 9199 9200 9201 9202 9203 9204
## [9205] 9205 9206 9207 9208 9209 9210 9211 9212 9213 9214 9215 9216
## [9217] 9217 9218 9219 9220 9221 9222 9223 9224 9225 9226 9227 9228
## [9229] 9229 9230 9231 9232 9233 9234 9235 9236 9237 9238 9239 9240
## [9241] 9241 9242 9243 9244 9245 9246 9247 9248 9249 9250 9251 9252
## [9253] 9253 9254 9255 9256 9257 9258 9259 9260 9261 9262 9263 9264
## [9265] 9265 9266 9267 9268 9269 9270 9271 9272 9273 9274 9275 9276
## [9277] 9277 9278 9279 9280 9281 9282 9283 9284 9285 9286 9287 9288
## [9289] 9289 9290 9291 9292 9293 9294 9295 9296 9297 9298 9299 9300
## [9301] 9301 9302 9303 9304 9305 9306 9307 9308 9309 9310 9311 9312
## [9313] 9313 9314 9315 9316 9317 9318 9319 9320 9321 9322 9323 9324
## [9325] 9325 9326 9327 9328 9329 9330 9331 9332 9333 9334 9335 9336
## [9337] 9337 9338 9339 9340 9341 9342 9343 9344 9345 9346 9347 9348
## [9349] 9349 9350 9351 9352 9353 9354 9355 9356 9357 9358 9359 9360
## [9361] 9361 9362 9363 9364 9365 9366 9367 9368 9369 9370 9371 9372
## [9373] 9373 9374 9375 9376 9377 9378 9379 9380 9381 9382 9383 9384
## [9385] 9385 9386 9387 9388 9389 9390 9391 9392 9393 9394 9395 9396
## [9397] 9397 9398 9399 9400 9401 9402 9403 9404 9405 9406 9407 9408
## [9409] 9409 9410 9411 9412 9413 9414 9415 9416 9417 9418 9419 9420
## [9421] 9421 9422 9423 9424 9425 9426 9427 9428 9429 9430 9431 9432
## [9433] 9433 9434 9435 9436 9437 9438 9439 9440 9441 9442 9443 9444
## [9445] 9445 9446 9447 9448 9449 9450 9451 9452 9453 9454 9455 9456
## [9457] 9457 9458 9459 9460 9461 9462 9463 9464 9465 9466 9467 9468
## [9469] 9469 9470 9471 9472 9473 9474 9475 9476 9477 9478 9479 9480
## [9481] 9481 9482 9483 9484 9485 9486 9487 9488 9489 9490 9491 9492
## [9493] 9493 9494 9495 9496 9497 9498 9499 9500 9501 9502 9503 9504
## [9505] 9505 9506 9507 9508 9509 9510 9511 9512 9513 9514 9515 9516
## [9517] 9517 9518 9519 9520 9521 9522 9523 9524 9525 9526 9527 9528
## [9529] 9529 9530 9531 9532 9533 9534 9535 9536 9537 9538 9539 9540
## [9541] 9541 9542 9543 9544 9545 9546 9547 9548 9549 9550 9551 9552
## [9553] 9553 9554 9555 9556 9557 9558 9559 9560 9561 9562 9563 9564
## [9565] 9565 9566 9567 9568 9569 9570 9571 9572 9573 9574 9575 9576
## [9577] 9577 9578 9579 9580 9581 9582 9583 9584 9585 9586 9587 9588
## [9589] 9589 9590 9591 9592 9593 9594 9595 9596 9597 9598 9599 9600
## [9601] 9601 9602 9603 9604 9605 9606 9607 9608 9609 9610 9611 9612
## [9613] 9613 9614 9615 9616 9617 9618 9619 9620 9621 9622 9623 9624
## [9625] 9625 9626 9627 9628 9629 9630 9631 9632 9633 9634 9635 9636
## [9637] 9637 9638 9639 9640 9641 9642 9643 9644 9645 9646 9647 9648
## [9649] 9649 9650 9651 9652 9653 9654 9655 9656 9657 9658 9659 9660
## [9661] 9661 9662 9663 9664 9665 9666 9667 9668 9669 9670 9671 9672
## [9673] 9673 9674 9675 9676 9677 9678 9679 9680 9681 9682 9683 9684
## [9685] 9685 9686 9687 9688 9689 9690 9691 9692 9693 9694 9695 9696
## [9697] 9697 9698 9699 9700 9701 9702 9703 9704 9705 9706 9707 9708
## [9709] 9709 9710 9711 9712 9713 9714 9715 9716 9717 9718 9719 9720
## [9721] 9721 9722 9723 9724 9725 9726 9727 9728 9729 9730 9731 9732
## [9733] 9733 9734 9735 9736 9737 9738 9739 9740 9741 9742 9743 9744
## [9745] 9745 9746 9747 9748 9749 9750 9751 9752 9753 9754 9755 9756
## [9757] 9757 9758 9759 9760 9761 9762 9763 9764 9765 9766 9767 9768
## [9769] 9769 9770 9771 9772 9773 9774 9775 9776 9777 9778 9779 9780
## [9781] 9781 9782 9783 9784 9785 9786 9787 9788 9789 9790 9791 9792
## [9793] 9793 9794 9795 9796 9797 9798 9799 9800 9801 9802 9803 9804
## [9805] 9805 9806 9807 9808 9809 9810 9811 9812 9813 9814 9815 9816
## [9817] 9817 9818 9819 9820 9821 9822 9823 9824 9825 9826 9827 9828
## [9829] 9829 9830 9831 9832 9833 9834 9835 9836 9837 9838 9839 9840
## [9841] 9841 9842 9843 9844 9845 9846 9847 9848 9849 9850 9851 9852
## [9853] 9853 9854 9855 9856 9857 9858 9859 9860 9861 9862 9863 9864
## [9865] 9865 9866 9867 9868 9869 9870 9871 9872 9873 9874 9875 9876
## [9877] 9877 9878 9879 9880 9881 9882 9883 9884 9885 9886 9887 9888
## [9889] 9889 9890 9891 9892 9893 9894 9895 9896 9897 9898 9899 9900
## [9901] 9901 9902 9903 9904 9905 9906 9907 9908 9909 9910 9911 9912
## [9913] 9913 9914 9915 9916 9917 9918 9919 9920 9921 9922 9923 9924
## [9925] 9925 9926 9927 9928 9929 9930 9931 9932 9933 9934 9935 9936
## [9937] 9937 9938 9939 9940 9941 9942 9943 9944 9945 9946 9947 9948
## [9949] 9949 9950 9951 9952 9953 9954 9955 9956 9957 9958 9959 9960
## [9961] 9961 9962 9963 9964 9965 9966 9967 9968 9969 9970 9971 9972
## [9973] 9973 9974 9975 9976 9977 9978 9979 9980 9981 9982 9983 9984
## [9985] 9985 9986 9987 9988 9989 9990 9991 9992 9993 9994 9995 9996
## [9997] 9997 9998 9999 10000 10001 10002 10003 10004 10005 10006 10007 10008
## [10009] 10009 10010 10011 10012 10013 10014 10015 10016 10017 10018 10019 10020
## [10021] 10021 10022 10023 10024 10025 10026 10027 10028 10029 10030 10031 10032
## [10033] 10033 10034 10035 10036 10037 10038 10039 10040 10041 10042 10043 10044
## [10045] 10045 10046 10047 10048 10049 10050 10051 10052 10053 10054 10055 10056
## [10057] 10057 10058 10059 10060 10061 10062 10063 10064 10065 10066 10067 10068
## [10069] 10069 10070 10071 10072 10073 10074 10075 10076 10077 10078 10079 10080
## [10081] 10081 10082 10083 10084 10085 10086 10087 10088 10089 10090 10091 10092
## [10093] 10093 10094 10095 10096 10097 10098 10099 10100 10101 10102 10103 10104
## [10105] 10105 10106 10107 10108 10109 10110 10111 10112 10113 10114 10115 10116
## [10117] 10117 10118 10119 10120 10121 10122 10123 10124 10125 10126 10127 10128
## [10129] 10129 10130 10131 10132 10133 10134 10135 10136 10137 10138 10139 10140
## [10141] 10141 10142 10143 10144 10145 10146 10147 10148 10149 10150 10151 10152
## [10153] 10153 10154 10155 10156 10157 10158 10159 10160 10161 10162 10163 10164
## [10165] 10165 10166 10167 10168 10169 10170 10171 10172 10173 10174 10175 10176
## [10177] 10177 10178 10179 10180 10181 10182 10183 10184 10185 10186 10187 10188
## [10189] 10189 10190 10191 10192 10193 10194 10195 10196 10197 10198 10199 10200
## [10201] 10201 10202 10203 10204 10205 10206 10207 10208 10209 10210 10211 10212
## [10213] 10213 10214 10215 10216 10217 10218 10219 10220 10221 10222 10223 10224
## [10225] 10225 10226 10227 10228 10229 10230 10231 10232 10233 10234 10235 10236
## [10237] 10237 10238 10239 10240 10241 10242 10243 10244 10245 10246 10247 10248
## [10249] 10249 10250 10251 10252 10253 10254 10255 10256 10257 10258 10259 10260
## [10261] 10261 10262 10263 10264 10265 10266 10267 10268 10269 10270 10271 10272
## [10273] 10273 10274 10275 10276 10277 10278 10279 10280 10281 10282 10283 10284
## [10285] 10285 10286 10287 10288 10289 10290 10291 10292 10293 10294 10295 10296
## [10297] 10297 10298 10299 10300 10301 10302 10303 10304 10305 10306 10307 10308
## [10309] 10309 10310 10311 10312 10313 10314 10315 10316 10317 10318 10319 10320
## [10321] 10321 10322 10323 10324 10325 10326 10327 10328 10329 10330 10331 10332
## [10333] 10333 10334 10335 10336 10337 10338 10339 10340 10341 10342 10343 10344
## [10345] 10345 10346 10347 10348 10349 10350 10351 10352 10353 10354 10355 10356
## [10357] 10357 10358 10359 10360 10361 10362 10363 10364 10365 10366 10367 10368
## [10369] 10369 10370 10371 10372 10373 10374 10375 10376 10377 10378 10379 10380
## [10381] 10381 10382 10383 10384 10385 10386 10387 10388 10389 10390 10391 10392
## [10393] 10393 10394 10395 10396 10397 10398 10399 10400 10401 10402 10403 10404
## [10405] 10405 10406 10407 10408 10409 10410 10411 10412 10413 10414 10415 10416
## [10417] 10417 10418 10419 10420 10421 10422 10423 10424 10425 10426 10427 10428
## [10429] 10429 10430 10431 10432 10433 10434 10435 10436 10437 10438 10439 10440
## [10441] 10441 10442 10443 10444 10445 10446 10447 10448 10449 10450 10451 10452
## [10453] 10453 10454 10455 10456 10457 10458 10459 10460 10461 10462 10463 10464
## [10465] 10465 10466 10467 10468 10469 10470 10471 10472 10473 10474 10475 10476
## [10477] 10477 10478 10479 10480 10481 10482 10483 10484 10485 10486 10487 10488
## [10489] 10489 10490 10491 10492 10493 10494 10495 10496 10497 10498 10499 10500
## [10501] 10501 10502 10503 10504 10505 10506 10507 10508 10509 10510 10511 10512
## [10513] 10513 10514 10515 10516 10517 10518 10519 10520 10521 10522 10523 10524
## [10525] 10525 10526 10527 10528 10529 10530 10531 10532 10533 10534 10535 10536
## [10537] 10537 10538 10539 10540 10541 10542 10543 10544 10545 10546 10547 10548
## [10549] 10549 10550 10551 10552 10553 10554 10555 10556 10557 10558 10559 10560
## [10561] 10561 10562 10563 10564 10565 10566 10567 10568 10569 10570 10571 10572
## [10573] 10573 10574 10575 10576 10577 10578 10579 10580 10581 10582 10583 10584
## [10585] 10585 10586 10587 10588 10589 10590 10591 10592 10593 10594 10595 10596
## [10597] 10597 10598 10599 10600 10601 10602 10603 10604 10605 10606 10607 10608
## [10609] 10609 10610 10611 10612 10613 10614 10615 10616 10617 10618 10619 10620
## [10621] 10621 10622 10623 10624 10625 10626 10627 10628 10629 10630 10631 10632
## [10633] 10633 10634 10635 10636 10637 10638 10639 10640 10641 10642 10643 10644
## [10645] 10645 10646 10647 10648 10649 10650 10651 10652 10653 10654 10655 10656
## [10657] 10657 10658 10659 10660 10661 10662 10663 10664 10665 10666 10667 10668
## [10669] 10669 10670 10671 10672 10673 10674 10675 10676 10677 10678 10679 10680
## [10681] 10681 10682 10683 10684 10685 10686 10687 10688 10689 10690 10691 10692
## [10693] 10693 10694 10695 10696 10697 10698 10699 10700 10701 10702 10703 10704
## [10705] 10705 10706 10707 10708 10709 10710 10711 10712 10713 10714 10715 10716
## [10717] 10717 10718 10719 10720 10721 10722 10723 10724 10725 10726 10727 10728
## [10729] 10729 10730 10731 10732 10733 10734 10735 10736 10737 10738 10739 10740
## [10741] 10741 10742 10743 10744 10745 10746 10747 10748 10749 10750 10751 10752
## [10753] 10753 10754 10755 10756 10757 10758 10759 10760 10761 10762 10763 10764
## [10765] 10765 10766 10767 10768 10769 10770 10771 10772 10773 10774 10775 10776
## [10777] 10777 10778 10779 10780 10781 10782 10783 10784 10785 10786 10787 10788
## [10789] 10789 10790 10791 10792 10793 10794 10795 10796 10797 10798 10799 10800
## [10801] 10801 10802 10803 10804 10805 10806 10807 10808 10809 10810 10811 10812
## [10813] 10813 10814 10815 10816 10817 10818 10819 10820 10821 10822 10823 10824
## [10825] 10825 10826 10827 10828 10829 10830 10831 10832 10833 10834 10835 10836
## [10837] 10837 10838 10839 10840 10841 10842 10843 10844 10845 10846 10847 10848
## [10849] 10849 10850 10851 10852 10853 10854 10855 10856 10857 10858 10859 10860
## [10861] 10861 10862 10863 10864 10865 10866 10867 10868 10869 10870 10871 10872
## [10873] 10873 10874 10875 10876 10877 10878 10879 10880 10881 10882 10883 10884
## [10885] 10885 10886 10887 10888 10889 10890 10891 10892 10893 10894 10895 10896
## [10897] 10897 10898 10899 10900 10901 10902 10903 10904 10905 10906 10907 10908
## [10909] 10909 10910 10911 10912 10913 10914 10915 10916 10917 10918 10919 10920
## [10921] 10921 10922 10923 10924 10925 10926 10927 10928 10929 10930 10931 10932
## [10933] 10933 10934 10935 10936 10937 10938 10939 10940 10941 10942 10943 10944
## [10945] 10945 10946 10947 10948 10949 10950 10951 10952 10953 10954 10955 10956
## [10957] 10957 10958 10959 10960 10961 10962 10963 10964 10965 10966 10967 10968
## [10969] 10969 10970 10971 10972 10973 10974 10975 10976 10977 10978 10979 10980
## [10981] 10981 10982 10983 10984 10985 10986 10987 10988 10989 10990 10991 10992
## [10993] 10993 10994 10995 10996 10997 10998 10999 11000 11001 11002 11003 11004
## [11005] 11005 11006 11007 11008 11009 11010 11011 11012 11013 11014 11015 11016
## [11017] 11017 11018 11019 11020 11021 11022 11023 11024 11025 11026 11027 11028
## [11029] 11029 11030 11031 11032 11033 11034 11035 11036 11037 11038 11039 11040
## [11041] 11041 11042 11043 11044 11045 11046 11047 11048 11049 11050 11051 11052
## [11053] 11053 11054 11055 11056 11057 11058 11059 11060 11061 11062 11063 11064
## [11065] 11065 11066 11067 11068 11069 11070 11071 11072 11073 11074 11075 11076
## [11077] 11077 11078 11079 11080 11081 11082 11083 11084 11085 11086 11087 11088
## [11089] 11089 11090 11091 11092 11093 11094 11095 11096 11097 11098 11099 11100
## [11101] 11101 11102 11103 11104 11105 11106 11107 11108 11109 11110 11111 11112
## [11113] 11113 11114 11115 11116 11117 11118 11119 11120 11121 11122 11123 11124
## [11125] 11125 11126 11127 11128 11129 11130 11131 11132 11133 11134 11135 11136
## [11137] 11137 11138 11139 11140 11141 11142 11143 11144 11145 11146 11147 11148
## [11149] 11149 11150 11151 11152 11153 11154 11155 11156 11157 11158 11159 11160
## [11161] 11161 11162 11163 11164 11165 11166 11167 11168 11169 11170 11171 11172
## [11173] 11173 11174 11175 11176 11177 11178 11179 11180 11181 11182 11183 11184
## [11185] 11185 11186 11187 11188 11189 11190 11191 11192 11193 11194 11195 11196
## [11197] 11197 11198 11199 11200 11201 11202 11203 11204 11205 11206 11207 11208
## [11209] 11209 11210 11211 11212 11213 11214 11215 11216 11217 11218 11219 11220
## [11221] 11221 11222 11223 11224 11225 11226 11227 11228 11229 11230 11231 11232
## [11233] 11233 11234 11235 11236 11237 11238 11239 11240 11241 11242 11243 11244
## [11245] 11245 11246 11247 11248 11249 11250 11251 11252 11253 11254 11255 11256
## [11257] 11257 11258 11259 11260 11261 11262 11263 11264 11265 11266 11267 11268
## [11269] 11269 11270 11271 11272 11273 11274 11275 11276 11277 11278 11279 11280
## [11281] 11281 11282 11283 11284 11285 11286 11287 11288 11289 11290 11291 11292
## [11293] 11293 11294 11295 11296 11297 11298 11299 11300 11301 11302 11303 11304
## [11305] 11305 11306 11307 11308 11309 11310 11311 11312 11313 11314 11315 11316
## [11317] 11317 11318 11319 11320 11321 11322 11323 11324 11325 11326 11327 11328
## [11329] 11329 11330 11331 11332 11333 11334 11335 11336 11337 11338 11339 11340
## [11341] 11341 11342 11343 11344 11345 11346 11347 11348 11349 11350 11351 11352
## [11353] 11353 11354 11355 11356 11357 11358 11359 11360 11361 11362 11363 11364
## [11365] 11365 11366 11367 11368 11369 11370 11371 11372 11373 11374 11375 11376
## [11377] 11377 11378 11379 11380 11381 11382 11383 11384 11385 11386 11387 11388
## [11389] 11389 11390 11391 11392 11393 11394 11395 11396 11397 11398 11399 11400
## [11401] 11401 11402 11403 11404 11405 11406 11407 11408 11409 11410 11411 11412
## [11413] 11413 11414 11415 11416 11417 11418 11419 11420 11421 11422 11423 11424
## [11425] 11425 11426 11427 11428 11429 11430 11431 11432 11433 11434 11435 11436
## [11437] 11437 11438 11439 11440 11441 11442 11443 11444 11445 11446 11447 11448
## [11449] 11449 11450 11451 11452 11453 11454 11455 11456 11457 11458 11459 11460
## [11461] 11461 11462 11463 11464 11465 11466 11467 11468 11469 11470 11471 11472
## [11473] 11473 11474 11475 11476 11477 11478 11479 11480 11481 11482 11483 11484
## [11485] 11485 11486 11487 11488 11489 11490 11491 11492 11493 11494 11495 11496
## [11497] 11497 11498 11499 11500 11501 11502 11503 11504 11505 11506 11507 11508
## [11509] 11509 11510 11511 11512 11513 11514 11515 11516 11517 11518 11519 11520
## [11521] 11521 11522 11523 11524 11525 11526 11527 11528 11529 11530 11531 11532
## [11533] 11533 11534 11535 11536 11537 11538 11539 11540 11541 11542 11543 11544
## [11545] 11545 11546 11547 11548 11549 11550 11551 11552 11553 11554 11555 11556
## [11557] 11557 11558 11559 11560 11561 11562 11563 11564 11565 11566 11567 11568
## [11569] 11569 11570 11571 11572 11573 11574 11575 11576 11577 11578 11579 11580
## [11581] 11581 11582 11583 11584 11585 11586 11587 11588 11589 11590 11591 11592
## [11593] 11593 11594 11595 11596 11597 11598 11599 11600 11601 11602 11603 11604
## [11605] 11605 11606 11607 11608 11609 11610 11611 11612 11613 11614 11615 11616
## [11617] 11617 11618 11619 11620 11621 11622 11623 11624 11625 11626 11627 11628
## [11629] 11629 11630 11631 11632 11633 11634 11635 11636 11637 11638 11639 11640
## [11641] 11641 11642 11643 11644 11645 11646 11647 11648 11649 11650 11651 11652
## [11653] 11653 11654 11655 11656 11657 11658 11659 11660 11661 11662 11663 11664
## [11665] 11665 11666 11667 11668 11669 11670 11671 11672 11673 11674 11675 11676
## [11677] 11677 11678 11679 11680 11681 11682 11683 11684 11685 11686 11687 11688
## [11689] 11689 11690 11691 11692 11693 11694 11695 11696 11697 11698 11699 11700
## [11701] 11701 11702 11703 11704 11705 11706 11707 11708 11709 11710 11711 11712
## [11713] 11713 11714 11715 11716 11717 11718 11719 11720 11721 11722 11723 11724
## [11725] 11725 11726 11727 11728 11729 11730 11731 11732 11733 11734 11735 11736
## [11737] 11737 11738 11739 11740 11741 11742 11743 11744 11745 11746 11747 11748
## [11749] 11749 11750 11751 11752 11753 11754 11755 11756 11757 11758 11759 11760
## [11761] 11761 11762 11763 11764 11765 11766 11767 11768 11769 11770 11771 11772
## [11773] 11773 11774 11775 11776 11777 11778 11779 11780 11781 11782 11783 11784
## [11785] 11785 11786 11787 11788 11789 11790 11791 11792 11793 11794 11795 11796
## [11797] 11797 11798 11799 11800 11801 11802 11803 11804 11805 11806 11807 11808
## [11809] 11809 11810 11811 11812 11813 11814 11815 11816 11817 11818 11819 11820
## [11821] 11821 11822 11823 11824 11825 11826 11827 11828 11829 11830 11831 11832
## [11833] 11833 11834 11835 11836 11837 11838 11839 11840 11841 11842 11843 11844
## [11845] 11845 11846 11847 11848 11849 11850 11851 11852 11853 11854 11855 11856
## [11857] 11857 11858 11859 11860 11861 11862 11863 11864 11865 11866 11867 11868
## [11869] 11869 11870 11871 11872 11873 11874 11875 11876 11877 11878 11879 11880
## [11881] 11881 11882 11883 11884 11885 11886 11887 11888 11889 11890 11891 11892
## [11893] 11893 11894 11895 11896 11897 11898 11899 11900 11901 11902 11903 11904
## [11905] 11905 11906 11907 11908 11909 11910 11911 11912 11913 11914 11915 11916
## [11917] 11917 11918 11919 11920 11921 11922 11923 11924 11925 11926 11927 11928
## [11929] 11929 11930 11931 11932 11933 11934 11935 11936 11937 11938 11939 11940
## [11941] 11941 11942 11943 11944 11945 11946 11947 11948 11949 11950 11951 11952
## [11953] 11953 11954 11955 11956 11957 11958 11959 11960 11961 11962 11963 11964
## [11965] 11965 11966 11967 11968 11969 11970 11971 11972 11973 11974 11975 11976
## [11977] 11977 11978 11979 11980 11981 11982 11983 11984 11985 11986 11987 11988
## [11989] 11989 11990 11991 11992 11993 11994 11995 11996 11997 11998 11999 12000
## [12001] 12001 12002 12003 12004 12005 12006 12007 12008 12009 12010 12011 12012
## [12013] 12013 12014 12015 12016 12017 12018 12019 12020 12021 12022 12023 12024
## [12025] 12025 12026 12027 12028 12029 12030 12031 12032 12033 12034 12035 12036
## [12037] 12037 12038 12039 12040 12041 12042 12043 12044 12045 12046 12047 12048
## [12049] 12049 12050 12051 12052 12053 12054 12055 12056 12057 12058 12059 12060
## [12061] 12061 12062 12063 12064 12065 12066 12067 12068 12069 12070 12071 12072
## [12073] 12073 12074 12075 12076 12077 12078 12079 12080 12081 12082 12083 12084
## [12085] 12085 12086 12087 12088 12089 12090 12091 12092 12093 12094 12095 12096
## [12097] 12097 12098 12099 12100 12101 12102 12103 12104 12105 12106 12107 12108
## [12109] 12109 12110 12111 12112 12113 12114 12115 12116 12117 12118 12119 12120
## [12121] 12121 12122 12123 12124 12125 12126 12127 12128 12129 12130 12131 12132
## [12133] 12133 12134 12135 12136 12137 12138 12139 12140 12141 12142 12143 12144
## [12145] 12145 12146 12147 12148 12149 12150 12151 12152 12153 12154 12155 12156
## [12157] 12157 12158 12159 12160 12161 12162 12163 12164 12165 12166 12167 12168
## [12169] 12169 12170 12171 12172 12173 12174 12175 12176 12177 12178 12179 12180
## [12181] 12181 12182 12183 12184 12185 12186 12187 12188 12189 12190 12191 12192
## [12193] 12193 12194 12195 12196 12197 12198 12199 12200 12201 12202 12203 12204
## [12205] 12205 12206 12207 12208 12209 12210 12211 12212 12213 12214 12215 12216
## [12217] 12217 12218 12219 12220 12221 12222 12223 12224 12225 12226 12227 12228
## [12229] 12229 12230 12231 12232 12233 12234 12235 12236 12237 12238 12239 12240
## [12241] 12241 12242 12243 12244 12245 12246 12247 12248 12249 12250 12251 12252
## [12253] 12253 12254 12255 12256 12257 12258 12259 12260 12261 12262 12263 12264
## [12265] 12265 12266 12267 12268 12269 12270 12271 12272 12273 12274 12275 12276
## [12277] 12277 12278 12279 12280 12281 12282 12283 12284 12285 12286 12287 12288
## [12289] 12289 12290 12291 12292 12293 12294 12295 12296 12297 12298 12299 12300
## [12301] 12301 12302 12303 12304 12305 12306 12307 12308 12309 12310 12311 12312
## [12313] 12313 12314 12315 12316 12317 12318 12319 12320 12321 12322 12323 12324
## [12325] 12325 12326 12327 12328 12329 12330 12331 12332 12333 12334 12335 12336
## [12337] 12337 12338 12339 12340 12341 12342 12343 12344 12345 12346 12347 12348
## [12349] 12349 12350 12351 12352 12353 12354 12355 12356 12357 12358 12359 12360
## [12361] 12361 12362 12363 12364 12365 12366 12367 12368 12369 12370 12371 12372
## [12373] 12373 12374 12375 12376 12377 12378 12379 12380 12381 12382 12383 12384
## [12385] 12385 12386 12387 12388 12389 12390 12391 12392 12393 12394 12395 12396
## [12397] 12397 12398 12399 12400 12401 12402 12403 12404 12405 12406 12407 12408
## [12409] 12409 12410 12411 12412 12413 12414 12415 12416 12417 12418 12419 12420
## [12421] 12421 12422 12423 12424 12425 12426 12427 12428 12429 12430 12431 12432
## [12433] 12433 12434 12435 12436 12437 12438 12439 12440 12441 12442 12443 12444
## [12445] 12445 12446 12447 12448 12449 12450 12451 12452 12453 12454 12455 12456
## [12457] 12457 12458 12459 12460 12461 12462 12463 12464 12465 12466 12467 12468
## [12469] 12469 12470 12471 12472 12473 12474 12475 12476 12477 12478 12479 12480
## [12481] 12481 12482 12483 12484 12485 12486 12487 12488 12489 12490 12491 12492
## [12493] 12493 12494 12495 12496 12497 12498 12499 12500 12501 12502 12503 12504
## [12505] 12505 12506 12507 12508 12509 12510 12511 12512 12513 12514 12515 12516
## [12517] 12517 12518 12519 12520 12521 12522 12523 12524 12525 12526 12527 12528
## [12529] 12529 12530 12531 12532 12533 12534 12535 12536 12537 12538 12539 12540
## [12541] 12541 12542 12543 12544 12545 12546 12547 12548 12549 12550 12551 12552
## [12553] 12553 12554 12555 12556 12557 12558 12559 12560 12561 12562 12563 12564
## [12565] 12565 12566 12567 12568 12569 12570 12571 12572 12573 12574 12575 12576
## [12577] 12577 12578 12579 12580 12581 12582 12583 12584 12585 12586 12587 12588
## [12589] 12589 12590 12591 12592 12593 12594 12595 12596 12597 12598 12599 12600
## [12601] 12601 12602 12603 12604 12605 12606 12607 12608 12609 12610 12611 12612
## [12613] 12613 12614 12615 12616 12617 12618 12619 12620 12621 12622 12623 12624
## [12625] 12625 12626 12627 12628 12629 12630 12631 12632 12633 12634 12635 12636
## [12637] 12637 12638 12639 12640 12641 12642 12643 12644 12645 12646 12647 12648
## [12649] 12649 12650 12651 12652 12653 12654 12655 12656 12657 12658 12659 12660
## [12661] 12661 12662 12663 12664 12665 12666 12667 12668 12669 12670 12671 12672
## [12673] 12673 12674 12675 12676 12677 12678 12679 12680 12681 12682 12683 12684
## [12685] 12685 12686 12687 12688 12689 12690 12691 12692 12693 12694 12695 12696
## [12697] 12697 12698 12699 12700 12701 12702 12703 12704 12705 12706 12707 12708
## [12709] 12709 12710 12711 12712 12713 12714 12715 12716 12717 12718 12719 12720
## [12721] 12721 12722 12723 12724 12725 12726 12727 12728 12729 12730 12731 12732
## [12733] 12733 12734 12735 12736 12737 12738 12739 12740 12741 12742 12743 12744
## [12745] 12745 12746 12747 12748 12749 12750 12751 12752 12753 12754 12755 12756
## [12757] 12757 12758 12759 12760 12761 12762 12763 12764 12765 12766 12767 12768
## [12769] 12769 12770 12771 12772 12773 12774 12775 12776 12777 12778 12779 12780
## [12781] 12781 12782 12783 12784 12785 12786 12787 12788 12789 12790 12791 12792
## [12793] 12793 12794 12795 12796 12797 12798 12799 12800 12801 12802 12803 12804
## [12805] 12805 12806 12807 12808 12809 12810 12811 12812 12813 12814 12815 12816
## [12817] 12817 12818 12819 12820 12821 12822 12823 12824 12825 12826 12827 12828
## [12829] 12829 12830 12831 12832 12833 12834 12835 12836 12837 12838 12839 12840
## [12841] 12841 12842 12843 12844 12845 12846 12847 12848 12849 12850 12851 12852
## [12853] 12853 12854 12855 12856 12857 12858 12859 12860 12861 12862 12863 12864
## [12865] 12865 12866 12867 12868 12869 12870 12871 12872 12873 12874 12875 12876
## [12877] 12877 12878 12879 12880 12881 12882 12883 12884 12885 12886 12887 12888
## [12889] 12889 12890 12891 12892 12893 12894 12895 12896 12897 12898 12899 12900
## [12901] 12901 12902 12903 12904 12905 12906 12907 12908 12909 12910 12911 12912
## [12913] 12913 12914 12915 12916 12917 12918 12919 12920 12921 12922 12923 12924
## [12925] 12925 12926 12927 12928 12929 12930 12931 12932 12933 12934 12935 12936
## [12937] 12937 12938 12939 12940 12941 12942 12943 12944 12945 12946 12947 12948
## [12949] 12949 12950 12951 12952 12953 12954 12955 12956 12957 12958 12959 12960
## [12961] 12961 12962 12963 12964 12965 12966 12967 12968 12969 12970 12971 12972
## [12973] 12973 12974 12975 12976 12977 12978 12979 12980 12981 12982 12983 12984
## [12985] 12985 12986 12987 12988 12989 12990 12991 12992 12993 12994 12995 12996
## [12997] 12997 12998 12999 13000 13001 13002 13003 13004 13005 13006 13007 13008
## [13009] 13009 13010 13011 13012 13013 13014 13015 13016 13017 13018 13019 13020
## [13021] 13021 13022 13023 13024 13025 13026 13027 13028 13029 13030 13031 13032
## [13033] 13033 13034 13035 13036 13037 13038 13039 13040 13041 13042 13043 13044
## [13045] 13045 13046 13047 13048 13049 13050 13051 13052 13053 13054 13055 13056
## [13057] 13057 13058 13059 13060 13061 13062 13063 13064 13065 13066 13067 13068
## [13069] 13069 13070 13071 13072 13073 13074 13075 13076 13077 13078 13079 13080
## [13081] 13081 13082 13083 13084 13085 13086 13087 13088 13089 13090 13091 13092
## [13093] 13093 13094 13095 13096 13097 13098 13099 13100 13101 13102 13103 13104
## [13105] 13105 13106 13107 13108 13109 13110 13111 13112 13113 13114 13115 13116
## [13117] 13117 13118 13119 13120 13121 13122 13123 13124 13125 13126 13127 13128
## [13129] 13129 13130 13131 13132 13133 13134 13135 13136 13137 13138 13139 13140
## [13141] 13141 13142 13143 13144 13145 13146 13147 13148 13149 13150 13151 13152
## [13153] 13153 13154 13155 13156 13157 13158 13159 13160 13161 13162 13163 13164
## [13165] 13165 13166 13167 13168 13169 13170 13171 13172 13173 13174 13175 13176
## [13177] 13177 13178 13179 13180 13181 13182 13183 13184 13185 13186 13187 13188
## [13189] 13189 13190 13191 13192 13193 13194 13195 13196 13197 13198 13199 13200
## [13201] 13201 13202 13203 13204 13205 13206 13207 13208 13209 13210 13211 13212
## [13213] 13213 13214 13215 13216 13217 13218 13219 13220 13221 13222 13223 13224
## [13225] 13225 13226 13227 13228 13229 13230 13231 13232 13233 13234 13235 13236
## [13237] 13237 13238 13239 13240 13241 13242 13243 13244 13245 13246 13247 13248
## [13249] 13249 13250 13251 13252 13253 13254 13255 13256 13257 13258 13259 13260
## [13261] 13261 13262 13263 13264 13265 13266 13267 13268 13269 13270 13271 13272
## [13273] 13273 13274 13275 13276 13277 13278 13279 13280 13281 13282 13283 13284
## [13285] 13285 13286 13287 13288 13289 13290 13291 13292 13293 13294 13295 13296
## [13297] 13297 13298 13299 13300 13301 13302 13303 13304 13305 13306 13307 13308
## [13309] 13309 13310 13311 13312 13313 13314 13315 13316 13317 13318 13319 13320
## [13321] 13321 13322 13323 13324 13325 13326 13327 13328 13329 13330 13331 13332
## [13333] 13333 13334 13335 13336 13337 13338 13339 13340 13341 13342 13343 13344
## [13345] 13345 13346 13347 13348 13349 13350 13351 13352 13353 13354 13355 13356
## [13357] 13357 13358 13359 13360 13361 13362 13363 13364 13365 13366 13367 13368
## [13369] 13369 13370 13371 13372 13373 13374 13375 13376 13377 13378 13379 13380
## [13381] 13381 13382 13383 13384 13385 13386 13387 13388 13389 13390 13391 13392
## [13393] 13393 13394 13395 13396 13397 13398 13399 13400 13401 13402 13403 13404
## [13405] 13405 13406 13407 13408 13409 13410 13411 13412 13413 13414 13415 13416
## [13417] 13417 13418 13419 13420 13421 13422 13423 13424 13425 13426 13427 13428
## [13429] 13429 13430 13431 13432 13433 13434 13435 13436 13437 13438 13439 13440
## [13441] 13441 13442 13443 13444 13445 13446 13447 13448 13449 13450 13451 13452
## [13453] 13453 13454 13455 13456 13457 13458 13459 13460 13461 13462 13463 13464
## [13465] 13465 13466 13467 13468 13469 13470 13471 13472 13473 13474 13475 13476
## [13477] 13477 13478 13479 13480 13481 13482 13483 13484 13485 13486 13487 13488
## [13489] 13489 13490 13491 13492 13493 13494 13495 13496 13497 13498 13499 13500
## [13501] 13501 13502 13503 13504 13505 13506 13507 13508 13509 13510 13511 13512
## [13513] 13513 13514 13515 13516 13517 13518 13519 13520 13521 13522 13523 13524
## [13525] 13525 13526 13527 13528 13529 13530 13531 13532 13533 13534 13535 13536
## [13537] 13537 13538 13539 13540 13541 13542 13543 13544 13545 13546 13547 13548
## [13549] 13549 13550 13551 13552 13553 13554 13555 13556 13557 13558 13559 13560
## [13561] 13561 13562 13563 13564 13565 13566 13567 13568 13569 13570 13571 13572
## [13573] 13573 13574 13575 13576 13577 13578 13579 13580 13581 13582 13583 13584
## [13585] 13585 13586 13587 13588 13589 13590 13591 13592 13593 13594 13595 13596
## [13597] 13597 13598 13599 13600 13601 13602 13603 13604 13605 13606 13607 13608
## [13609] 13609 13610 13611 13612 13613 13614 13615 13616 13617 13618 13619 13620
## [13621] 13621 13622 13623 13624 13625 13626 13627 13628 13629 13630 13631 13632
## [13633] 13633 13634 13635 13636 13637 13638 13639 13640 13641 13642 13643 13644
## [13645] 13645 13646 13647 13648 13649 13650 13651 13652 13653 13654 13655 13656
## [13657] 13657 13658 13659 13660 13661 13662 13663 13664 13665 13666 13667 13668
## [13669] 13669 13670 13671 13672 13673 13674 13675 13676 13677 13678 13679 13680
## [13681] 13681 13682 13683 13684 13685 13686 13687 13688 13689 13690 13691 13692
## [13693] 13693 13694 13695 13696 13697 13698 13699 13700 13701 13702 13703 13704
## [13705] 13705 13706 13707 13708 13709 13710 13711 13712 13713 13714 13715 13716
## [13717] 13717 13718 13719 13720 13721 13722 13723 13724 13725 13726 13727 13728
## [13729] 13729 13730 13731 13732 13733 13734 13735 13736 13737 13738 13739 13740
## [13741] 13741 13742 13743 13744 13745 13746 13747 13748 13749 13750 13751 13752
## [13753] 13753 13754 13755 13756 13757 13758 13759 13760 13761 13762 13763 13764
## [13765] 13765 13766 13767 13768 13769 13770 13771 13772 13773 13774 13775 13776
## [13777] 13777 13778 13779 13780 13781 13782 13783 13784 13785 13786 13787 13788
## [13789] 13789 13790 13791 13792 13793 13794 13795 13796 13797 13798 13799 13800
## [13801] 13801 13802 13803 13804 13805 13806 13807 13808 13809 13810 13811 13812
## [13813] 13813 13814 13815 13816 13817 13818 13819 13820 13821 13822 13823 13824
## [13825] 13825 13826 13827 13828 13829 13830 13831 13832 13833 13834 13835 13836
## [13837] 13837 13838 13839 13840 13841 13842 13843 13844 13845 13846 13847 13848
## [13849] 13849 13850 13851 13852 13853 13854 13855 13856 13857 13858 13859 13860
## [13861] 13861 13862 13863 13864 13865 13866 13867 13868 13869 13870 13871 13872
## [13873] 13873 13874 13875 13876 13877 13878 13879 13880 13881 13882 13883 13884
## [13885] 13885 13886 13887 13888 13889 13890 13891 13892 13893 13894 13895 13896
## [13897] 13897 13898 13899 13900 13901 13902 13903 13904 13905 13906 13907 13908
## [13909] 13909 13910 13911 13912 13913 13914 13915 13916 13917 13918 13919 13920
## [13921] 13921 13922 13923 13924 13925 13926 13927 13928 13929 13930 13931 13932
## [13933] 13933 13934 13935 13936 13937 13938 13939 13940 13941 13942 13943 13944
## [13945] 13945 13946 13947 13948 13949 13950 13951 13952 13953 13954 13955 13956
## [13957] 13957 13958 13959 13960 13961 13962 13963 13964 13965 13966 13967 13968
## [13969] 13969 13970 13971 13972 13973 13974 13975 13976 13977 13978 13979 13980
## [13981] 13981 13982 13983 13984 13985 13986 13987 13988 13989 13990 13991 13992
## [13993] 13993 13994 13995 13996 13997 13998 13999 14000 14001 14002 14003 14004
## [14005] 14005 14006 14007 14008 14009 14010 14011 14012 14013 14014 14015 14016
## [14017] 14017 14018 14019 14020 14021 14022 14023 14024 14025 14026 14027 14028
## [14029] 14029 14030 14031 14032 14033 14034 14035 14036 14037 14038 14039 14040
## [14041] 14041 14042 14043 14044 14045 14046 14047 14048 14049 14050 14051 14052
## [14053] 14053 14054 14055 14056 14057 14058 14059 14060 14061 14062 14063 14064
## [14065] 14065 14066 14067 14068 14069 14070 14071 14072 14073 14074 14075 14076
## [14077] 14077 14078 14079 14080 14081 14082 14083 14084 14085 14086 14087 14088
## [14089] 14089 14090 14091 14092 14093 14094 14095 14096 14097 14098 14099 14100
## [14101] 14101 14102 14103 14104 14105 14106 14107 14108 14109 14110 14111 14112
## [14113] 14113 14114 14115 14116 14117 14118 14119 14120 14121 14122 14123 14124
## [14125] 14125 14126 14127 14128 14129 14130 14131 14132 14133 14134 14135 14136
## [14137] 14137 14138 14139 14140 14141 14142 14143 14144 14145 14146 14147 14148
## [14149] 14149 14150 14151 14152 14153 14154 14155 14156 14157 14158 14159 14160
## [14161] 14161 14162 14163 14164 14165 14166 14167 14168 14169 14170 14171 14172
## [14173] 14173 14174 14175 14176 14177 14178 14179 14180 14181 14182 14183 14184
## [14185] 14185 14186 14187 14188 14189 14190 14191 14192 14193 14194 14195 14196
## [14197] 14197 14198 14199 14200 14201 14202 14203 14204 14205 14206 14207 14208
## [14209] 14209 14210 14211 14212 14213 14214 14215 14216 14217 14218 14219 14220
## [14221] 14221 14222 14223 14224 14225 14226 14227 14228 14229 14230 14231 14232
## [14233] 14233 14234 14235 14236 14237 14238 14239 14240 14241 14242 14243 14244
## [14245] 14245 14246 14247 14248 14249 14250 14251 14252 14253 14254 14255 14256
## [14257] 14257 14258 14259 14260 14261 14262 14263 14264 14265 14266 14267 14268
## [14269] 14269 14270 14271 14272 14273 14274 14275 14276 14277 14278 14279 14280
## [14281] 14281 14282 14283 14284 14285 14286 14287 14288 14289 14290 14291 14292
## [14293] 14293 14294 14295 14296 14297 14298 14299 14300 14301 14302 14303 14304
## [14305] 14305 14306 14307 14308 14309 14310 14311 14312 14313 14314 14315 14316
## [14317] 14317 14318 14319 14320 14321 14322 14323 14324 14325 14326 14327 14328
## [14329] 14329 14330 14331 14332 14333 14334 14335 14336 14337 14338 14339 14340
## [14341] 14341 14342 14343 14344 14345 14346 14347 14348 14349 14350 14351 14352
## [14353] 14353 14354 14355 14356 14357 14358 14359 14360 14361 14362 14363 14364
## [14365] 14365 14366 14367 14368 14369 14370 14371 14372 14373 14374 14375 14376
## [14377] 14377 14378 14379 14380 14381 14382 14383 14384 14385 14386 14387 14388
## [14389] 14389 14390 14391 14392 14393 14394 14395 14396 14397 14398 14399 14400
## [14401] 14401 14402 14403 14404 14405 14406 14407 14408 14409 14410 14411 14412
## [14413] 14413 14414 14415 14416 14417 14418 14419 14420 14421 14422 14423 14424
## [14425] 14425 14426 14427 14428 14429 14430 14431 14432 14433 14434 14435 14436
## [14437] 14437 14438 14439 14440 14441 14442 14443 14444 14445 14446 14447 14448
## [14449] 14449 14450 14451 14452 14453 14454 14455 14456 14457 14458 14459 14460
## [14461] 14461 14462 14463 14464 14465 14466 14467 14468 14469 14470 14471 14472
## [14473] 14473 14474 14475 14476 14477 14478 14479 14480 14481 14482 14483 14484
## [14485] 14485 14486 14487 14488 14489 14490 14491 14492 14493 14494 14495 14496
## [14497] 14497 14498 14499 14500 14501 14502 14503 14504 14505 14506 14507 14508
## [14509] 14509 14510 14511 14512 14513 14514 14515 14516 14517 14518 14519 14520
## [14521] 14521 14522 14523 14524 14525 14526 14527 14528 14529 14530 14531 14532
## [14533] 14533 14534 14535 14536 14537 14538 14539 14540 14541 14542 14543 14544
## [14545] 14545 14546 14547 14548 14549 14550 14551 14552 14553 14554 14555 14556
## [14557] 14557 14558 14559 14560 14561 14562 14563 14564 14565 14566 14567 14568
## [14569] 14569 14570 14571 14572 14573 14574 14575 14576 14577 14578 14579 14580
## [14581] 14581 14582 14583 14584 14585 14586 14587 14588 14589 14590 14591 14592
## [14593] 14593 14594 14595 14596 14597 14598 14599 14600 14601 14602 14603 14604
## [14605] 14605 14606 14607 14608 14609 14610 14611 14612 14613 14614 14615 14616
## [14617] 14617 14618 14619 14620 14621 14622 14623 14624 14625 14626 14627 14628
## [14629] 14629 14630 14631 14632 14633 14634 14635 14636 14637 14638 14639 14640
## [14641] 14641 14642 14643 14644 14645 14646 14647 14648 14649 14650 14651 14652
## [14653] 14653 14654 14655 14656 14657 14658 14659 14660 14661 14662 14663 14664
## [14665] 14665 14666 14667 14668 14669 14670 14671 14672 14673 14674 14675 14676
## [14677] 14677 14678 14679 14680 14681 14682 14683 14684 14685 14686 14687 14688
## [14689] 14689 14690 14691 14692 14693 14694 14695 14696 14697 14698 14699 14700
## [14701] 14701 14702 14703 14704 14705 14706 14707 14708 14709 14710 14711 14712
## [14713] 14713 14714 14715 14716 14717 14718 14719 14720 14721 14722 14723 14724
## [14725] 14725 14726 14727 14728 14729 14730 14731 14732 14733 14734 14735 14736
## [14737] 14737 14738 14739 14740 14741 14742 14743 14744 14745 14746 14747 14748
## [14749] 14749 14750 14751 14752 14753 14754 14755 14756 14757 14758 14759 14760
## [14761] 14761 14762 14763 14764 14765 14766 14767 14768 14769 14770 14771 14772
## [14773] 14773 14774 14775 14776 14777 14778 14779 14780 14781 14782 14783 14784
## [14785] 14785 14786 14787 14788 14789 14790 14791 14792 14793 14794 14795 14796
## [14797] 14797 14798 14799 14800 14801 14802 14803 14804 14805 14806 14807 14808
## [14809] 14809 14810 14811 14812 14813 14814 14815 14816 14817 14818 14819 14820
## [14821] 14821 14822 14823 14824 14825 14826 14827 14828 14829 14830 14831 14832
## [14833] 14833 14834 14835 14836 14837 14838 14839 14840 14841 14842 14843 14844
## [14845] 14845 14846 14847 14848 14849 14850 14851 14852 14853 14854 14855 14856
## [14857] 14857 14858 14859 14860 14861 14862 14863 14864 14865 14866 14867 14868
## [14869] 14869 14870 14871 14872 14873 14874 14875 14876 14877 14878 14879 14880
## [14881] 14881 14882 14883 14884 14885 14886 14887 14888 14889 14890 14891 14892
## [14893] 14893 14894 14895 14896 14897 14898 14899 14900 14901 14902 14903 14904
## [14905] 14905 14906 14907 14908 14909 14910 14911 14912 14913 14914 14915 14916
## [14917] 14917 14918 14919 14920 14921 14922 14923 14924 14925 14926 14927 14928
## [14929] 14929 14930 14931 14932 14933 14934 14935 14936 14937 14938 14939 14940
## [14941] 14941 14942 14943 14944 14945 14946 14947 14948 14949 14950 14951 14952
## [14953] 14953 14954 14955 14956 14957 14958 14959 14960 14961 14962 14963 14964
## [14965] 14965 14966 14967 14968 14969 14970 14971 14972 14973 14974 14975 14976
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## [14989] 14989 14990 14991 14992 14993 14994 14995 14996 14997 14998 14999 15000
## [15001] 15001 15002 15003 15004 15005 15006 15007 15008 15009 15010 15011 15012
## [15013] 15013 15014 15015 15016 15017 15018 15019 15020 15021 15022 15023 15024
## [15025] 15025 15026 15027 15028 15029 15030 15031 15032 15033 15034 15035 15036
## [15037] 15037 15038 15039 15040 15041 15042 15043 15044 15045 15046 15047 15048
## [15049] 15049 15050 15051 15052 15053 15054 15055 15056 15057 15058 15059 15060
## [15061] 15061 15062 15063 15064 15065 15066 15067 15068 15069 15070 15071 15072
## [15073] 15073 15074 15075 15076 15077 15078 15079 15080 15081 15082 15083 15084
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## [15109] 15109 15110 15111 15112 15113 15114 15115 15116 15117 15118 15119 15120
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## [15133] 15133 15134 15135 15136 15137 15138 15139 15140 15141 15142 15143 15144
## [15145] 15145 15146 15147 15148 15149 15150 15151 15152 15153 15154 15155 15156
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## [15181] 15181 15182 15183 15184 15185 15186 15187 15188 15189 15190 15191 15192
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## [15217] 15217 15218 15219 15220 15221 15222 15223 15224 15225 15226 15227 15228
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## [15241] 15241 15242 15243 15244 15245 15246 15247 15248 15249 15250 15251 15252
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## [15277] 15277 15278 15279 15280 15281 15282 15283 15284 15285 15286 15287 15288
## [15289] 15289 15290 15291 15292 15293 15294 15295 15296 15297 15298 15299 15300
## [15301] 15301 15302 15303 15304 15305 15306 15307 15308 15309 15310 15311 15312
## [15313] 15313 15314 15315 15316 15317 15318 15319 15320 15321 15322 15323 15324
## [15325] 15325 15326 15327 15328 15329 15330 15331 15332 15333 15334 15335 15336
## [15337] 15337 15338 15339 15340 15341 15342 15343 15344 15345 15346 15347 15348
## [15349] 15349 15350 15351 15352 15353 15354 15355 15356 15357 15358 15359 15360
## [15361] 15361 15362 15363 15364 15365 15366 15367 15368 15369 15370 15371 15372
## [15373] 15373 15374 15375 15376 15377 15378 15379 15380 15381 15382 15383 15384
## [15385] 15385 15386 15387 15388 15389 15390 15391 15392 15393 15394 15395 15396
## [15397] 15397 15398 15399 15400 15401 15402 15403 15404 15405 15406 15407 15408
## [15409] 15409 15410 15411 15412 15413 15414 15415 15416 15417 15418 15419 15420
## [15421] 15421 15422 15423 15424 15425 15426 15427 15428 15429 15430 15431 15432
## [15433] 15433 15434 15435 15436 15437 15438 15439 15440 15441 15442 15443 15444
## [15445] 15445 15446 15447 15448 15449 15450 15451 15452 15453 15454 15455 15456
## [15457] 15457 15458 15459 15460 15461 15462 15463 15464 15465 15466 15467 15468
## [15469] 15469 15470 15471 15472 15473 15474 15475 15476 15477 15478 15479 15480
## [15481] 15481 15482 15483 15484 15485 15486 15487 15488 15489 15490 15491 15492
## [15493] 15493 15494 15495 15496 15497 15498 15499 15500 15501 15502 15503 15504
## [15505] 15505 15506 15507 15508 15509 15510 15511 15512 15513 15514 15515 15516
## [15517] 15517 15518 15519 15520 15521 15522 15523 15524 15525 15526 15527 15528
## [15529] 15529 15530 15531 15532 15533 15534 15535 15536 15537 15538 15539 15540
## [15541] 15541 15542 15543 15544 15545 15546 15547 15548 15549 15550 15551 15552
## [15553] 15553 15554 15555 15556 15557 15558 15559 15560 15561 15562 15563 15564
## [15565] 15565 15566 15567 15568 15569 15570 15571 15572 15573 15574 15575 15576
## [15577] 15577 15578 15579 15580 15581 15582 15583 15584 15585 15586 15587 15588
## [15589] 15589 15590 15591 15592 15593 15594 15595 15596 15597 15598 15599 15600
## [15601] 15601 15602 15603 15604 15605 15606 15607 15608 15609 15610 15611 15612
## [15613] 15613 15614 15615 15616 15617 15618 15619 15620 15621 15622 15623 15624
## [15625] 15625 15626 15627 15628 15629 15630 15631 15632 15633 15634 15635 15636
## [15637] 15637 15638 15639 15640 15641 15642 15643 15644 15645 15646 15647 15648
## [15649] 15649 15650 15651 15652 15653 15654 15655 15656 15657 15658 15659 15660
## [15661] 15661 15662 15663 15664 15665 15666 15667 15668 15669 15670 15671 15672
## [15673] 15673 15674 15675 15676 15677 15678 15679 15680 15681 15682 15683 15684
## [15685] 15685 15686 15687 15688 15689 15690 15691 15692 15693 15694 15695 15696
## [15697] 15697 15698 15699 15700 15701 15702 15703 15704 15705 15706 15707 15708
## [15709] 15709 15710 15711 15712 15713 15714 15715 15716 15717 15718 15719 15720
## [15721] 15721 15722 15723 15724 15725 15726 15727 15728 15729 15730 15731 15732
## [15733] 15733 15734 15735 15736 15737 15738 15739 15740 15741 15742 15743 15744
## [15745] 15745 15746 15747 15748 15749 15750 15751 15752 15753 15754 15755 15756
## [15757] 15757 15758 15759 15760 15761 15762 15763 15764 15765 15766 15767 15768
## [15769] 15769 15770 15771 15772 15773 15774 15775 15776 15777 15778 15779 15780
## [15781] 15781 15782 15783 15784 15785 15786 15787 15788 15789 15790 15791 15792
## [15793] 15793 15794 15795 15796 15797 15798 15799 15800 15801 15802 15803 15804
## [15805] 15805 15806 15807 15808 15809 15810 15811 15812 15813 15814 15815 15816
## [15817] 15817 15818 15819 15820 15821 15822 15823 15824 15825 15826 15827 15828
## [15829] 15829 15830 15831 15832 15833 15834 15835 15836 15837 15838 15839 15840
## [15841] 15841 15842 15843 15844 15845 15846 15847 15848 15849 15850 15851 15852
## [15853] 15853 15854 15855 15856 15857 15858 15859 15860 15861 15862 15863 15864
## [15865] 15865 15866 15867 15868 15869 15870 15871 15872 15873 15874 15875 15876
## [15877] 15877 15878 15879 15880 15881 15882 15883 15884 15885 15886 15887 15888
## [15889] 15889 15890 15891 15892 15893 15894 15895 15896 15897 15898 15899 15900
## [15901] 15901 15902 15903 15904 15905 15906 15907 15908 15909 15910 15911 15912
## [15913] 15913 15914 15915 15916 15917 15918 15919 15920 15921 15922 15923 15924
## [15925] 15925 15926 15927 15928 15929 15930 15931 15932 15933 15934 15935 15936
## [15937] 15937 15938 15939 15940 15941 15942 15943 15944 15945 15946 15947 15948
## [15949] 15949 15950 15951 15952 15953 15954 15955 15956 15957 15958 15959 15960
## [15961] 15961 15962 15963 15964 15965 15966 15967 15968 15969 15970 15971 15972
## [15973] 15973 15974 15975 15976 15977 15978 15979 15980 15981 15982 15983 15984
## [15985] 15985 15986 15987 15988 15989 15990 15991 15992 15993 15994 15995 15996
## [15997] 15997 15998 15999 16000 16001 16002 16003 16004 16005 16006 16007 16008
## [16009] 16009 16010 16011 16012 16013 16014 16015 16016 16017 16018 16019 16020
## [16021] 16021 16022 16023 16024 16025 16026 16027 16028 16029 16030 16031 16032
## [16033] 16033 16034 16035 16036 16037 16038 16039 16040 16041 16042 16043 16044
## [16045] 16045 16046 16047 16048 16049 16050 16051 16052 16053 16054 16055 16056
## [16057] 16057 16058 16059 16060 16061 16062 16063 16064 16065 16066 16067 16068
## [16069] 16069 16070 16071 16072 16073 16074 16075 16076 16077 16078 16079 16080
## [16081] 16081 16082 16083 16084 16085 16086 16087 16088 16089 16090 16091 16092
## [16093] 16093 16094 16095 16096 16097 16098 16099 16100 16101 16102 16103 16104
## [16105] 16105 16106 16107 16108 16109 16110 16111 16112 16113 16114 16115 16116
## [16117] 16117 16118 16119 16120 16121 16122 16123 16124 16125 16126 16127 16128
## [16129] 16129 16130 16131 16132 16133 16134 16135 16136 16137 16138 16139 16140
## [16141] 16141 16142 16143 16144 16145 16146 16147 16148 16149 16150 16151 16152
## [16153] 16153 16154 16155 16156 16157 16158 16159 16160 16161 16162 16163 16164
## [16165] 16165 16166 16167 16168 16169 16170 16171 16172 16173 16174 16175 16176
## [16177] 16177 16178 16179 16180 16181 16182 16183 16184 16185 16186 16187 16188
## [16189] 16189 16190 16191 16192 16193 16194 16195 16196 16197 16198 16199 16200
## [16201] 16201 16202 16203 16204 16205 16206 16207 16208 16209 16210 16211 16212
## [16213] 16213 16214 16215 16216 16217 16218 16219 16220 16221 16222 16223 16224
## [16225] 16225 16226 16227 16228 16229 16230 16231 16232 16233 16234 16235 16236
## [16237] 16237 16238 16239 16240 16241 16242 16243 16244 16245 16246 16247 16248
## [16249] 16249 16250 16251 16252 16253 16254 16255 16256 16257 16258 16259 16260
## [16261] 16261 16262 16263 16264 16265 16266 16267 16268 16269 16270 16271 16272
## [16273] 16273 16274 16275 16276 16277 16278 16279 16280 16281 16282 16283 16284
## [16285] 16285 16286 16287 16288 16289 16290 16291 16292 16293 16294 16295 16296
## [16297] 16297 16298 16299 16300 16301 16302 16303 16304 16305 16306 16307 16308
## [16309] 16309 16310 16311 16312 16313 16314 16315 16316 16317 16318 16319 16320
## [16321] 16321 16322 16323 16324 16325 16326 16327 16328 16329 16330 16331 16332
## [16333] 16333 16334 16335 16336 16337 16338 16339 16340 16341 16342 16343 16344
## [16345] 16345 16346 16347 16348 16349 16350 16351 16352 16353 16354 16355 16356
## [16357] 16357 16358 16359 16360 16361 16362 16363 16364 16365 16366 16367 16368
## [16369] 16369 16370 16371 16372 16373 16374 16375 16376 16377 16378 16379 16380
## [16381] 16381 16382 16383 16384 16385 16386 16387 16388 16389 16390 16391 16392
## [16393] 16393 16394 16395 16396 16397 16398 16399 16400 16401 16402 16403 16404
## [16405] 16405 16406 16407 16408 16409 16410 16411 16412 16413 16414 16415 16416
## [16417] 16417 16418 16419 16420 16421 16422 16423 16424 16425 16426 16427 16428
## [16429] 16429 16430 16431 16432 16433 16434 16435 16436 16437 16438 16439 16440
## [16441] 16441 16442 16443 16444 16445 16446 16447 16448 16449 16450 16451 16452
## [16453] 16453 16454 16455 16456 16457 16458 16459 16460 16461 16462 16463 16464
## [16465] 16465 16466 16467 16468 16469 16470 16471 16472 16473 16474 16475 16476
## [16477] 16477 16478 16479 16480 16481 16482 16483 16484 16485 16486 16487 16488
## [16489] 16489 16490 16491 16492 16493 16494 16495 16496 16497 16498 16499 16500
## [16501] 16501 16502 16503 16504 16505 16506 16507 16508 16509 16510 16511 16512
## [16513] 16513 16514 16515 16516 16517 16518 16519 16520 16521 16522 16523 16524
## [16525] 16525 16526 16527 16528 16529 16530 16531 16532 16533 16534 16535 16536
## [16537] 16537 16538 16539 16540 16541 16542 16543 16544 16545 16546 16547 16548
## [16549] 16549 16550 16551 16552 16553 16554 16555 16556 16557 16558 16559 16560
## [16561] 16561 16562 16563 16564 16565 16566 16567 16568 16569 16570 16571 16572
## [16573] 16573 16574 16575 16576 16577 16578 16579 16580 16581 16582 16583 16584
## [16585] 16585 16586 16587 16588 16589 16590 16591 16592 16593 16594 16595 16596
## [16597] 16597 16598 16599 16600 16601 16602 16603 16604 16605 16606 16607 16608
## [16609] 16609 16610 16611 16612 16613 16614 16615 16616 16617 16618 16619 16620
## [16621] 16621 16622 16623 16624 16625 16626 16627 16628 16629 16630 16631 16632
## [16633] 16633 16634 16635 16636 16637 16638 16639 16640 16641 16642 16643 16644
## [16645] 16645 16646 16647 16648 16649 16650 16651 16652 16653 16654 16655 16656
## [16657] 16657 16658 16659 16660 16661 16662 16663 16664 16665 16666 16667 16668
## [16669] 16669 16670 16671 16672 16673 16674 16675 16676 16677 16678 16679 16680
## [16681] 16681 16682 16683 16684 16685 16686 16687 16688 16689 16690 16691 16692
## [16693] 16693 16694 16695 16696 16697 16698 16699 16700 16701 16702 16703 16704
## [16705] 16705 16706 16707 16708 16709 16710 16711 16712 16713 16714 16715 16716
## [16717] 16717 16718 16719 16720 16721 16722 16723 16724 16725 16726 16727 16728
## [16729] 16729 16730 16731 16732 16733 16734 16735 16736 16737 16738 16739 16740
## [16741] 16741 16742 16743 16744 16745 16746 16747 16748 16749 16750 16751 16752
## [16753] 16753 16754 16755 16756 16757 16758 16759 16760 16761 16762 16763 16764
## [16765] 16765 16766 16767 16768 16769 16770 16771 16772 16773 16774 16775 16776
## [16777] 16777 16778 16779 16780 16781 16782 16783 16784 16785 16786 16787 16788
## [16789] 16789 16790 16791 16792 16793 16794 16795 16796 16797 16798 16799 16800
## [16801] 16801 16802 16803 16804 16805 16806 16807 16808 16809 16810 16811 16812
## [16813] 16813 16814 16815 16816 16817 16818 16819 16820 16821 16822 16823 16824
## [16825] 16825 16826 16827 16828 16829 16830 16831 16832 16833 16834 16835 16836
## [16837] 16837 16838 16839 16840 16841 16842 16843 16844 16845 16846 16847 16848
## [16849] 16849 16850 16851 16852 16853 16854 16855 16856 16857 16858 16859 16860
## [16861] 16861 16862 16863 16864 16865 16866 16867 16868 16869 16870 16871 16872
## [16873] 16873 16874 16875 16876 16877 16878 16879 16880 16881 16882 16883 16884
## [16885] 16885 16886 16887 16888 16889 16890 16891 16892 16893 16894 16895 16896
## [16897] 16897 16898 16899 16900 16901 16902 16903 16904 16905 16906 16907 16908
## [16909] 16909 16910 16911 16912 16913 16914 16915 16916 16917 16918 16919 16920
## [16921] 16921 16922 16923 16924 16925 16926 16927 16928 16929 16930 16931 16932
## [16933] 16933 16934 16935 16936 16937 16938 16939 16940 16941 16942 16943 16944
## [16945] 16945 16946 16947 16948 16949 16950 16951 16952 16953 16954 16955 16956
## [16957] 16957 16958 16959 16960 16961 16962 16963 16964 16965 16966 16967 16968
## [16969] 16969 16970 16971 16972 16973 16974 16975 16976 16977 16978 16979 16980
## [16981] 16981 16982 16983 16984 16985 16986 16987 16988 16989 16990 16991 16992
## [16993] 16993 16994 16995 16996 16997 16998 16999 17000 17001 17002 17003 17004
## [17005] 17005 17006 17007 17008 17009 17010 17011 17012 17013 17014 17015 17016
## [17017] 17017 17018 17019 17020 17021 17022 17023 17024 17025 17026 17027 17028
## [17029] 17029 17030 17031 17032 17033 17034 17035 17036 17037 17038 17039 17040
## [17041] 17041 17042 17043 17044 17045 17046 17047 17048 17049 17050 17051 17052
## [17053] 17053 17054 17055 17056 17057 17058 17059 17060 17061 17062 17063 17064
## [17065] 17065 17066 17067 17068 17069 17070 17071 17072 17073 17074 17075 17076
## [17077] 17077 17078 17079 17080 17081 17082 17083 17084 17085 17086 17087 17088
## [17089] 17089 17090 17091 17092 17093 17094 17095 17096 17097 17098 17099 17100
## [17101] 17101 17102 17103 17104 17105 17106 17107 17108 17109 17110 17111 17112
## [17113] 17113 17114 17115 17116 17117 17118 17119 17120 17121 17122 17123 17124
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## [17137] 17137 17138 17139 17140 17141 17142 17143 17144 17145 17146 17147 17148
## [17149] 17149 17150 17151 17152 17153 17154 17155 17156 17157 17158 17159 17160
## [17161] 17161 17162 17163 17164 17165 17166 17167 17168 17169 17170 17171 17172
## [17173] 17173 17174 17175 17176 17177 17178 17179 17180 17181 17182 17183 17184
## [17185] 17185 17186 17187 17188 17189 17190 17191 17192 17193 17194 17195 17196
## [17197] 17197 17198 17199 17200 17201 17202 17203 17204 17205 17206 17207 17208
## [17209] 17209 17210 17211 17212 17213 17214 17215 17216 17217 17218 17219 17220
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## [17233] 17233 17234 17235 17236 17237 17238 17239 17240 17241 17242 17243 17244
## [17245] 17245 17246 17247 17248 17249 17250 17251 17252 17253 17254 17255 17256
## [17257] 17257 17258 17259 17260 17261 17262 17263 17264 17265 17266 17267 17268
## [17269] 17269 17270 17271 17272 17273 17274 17275 17276 17277 17278 17279 17280
## [17281] 17281 17282 17283 17284 17285 17286 17287 17288 17289 17290 17291 17292
## [17293] 17293 17294 17295 17296 17297 17298 17299 17300 17301 17302 17303 17304
## [17305] 17305 17306 17307 17308 17309 17310 17311 17312 17313 17314 17315 17316
## [17317] 17317 17318 17319 17320 17321 17322 17323 17324 17325 17326 17327 17328
## [17329] 17329 17330 17331 17332 17333 17334 17335 17336 17337 17338 17339 17340
## [17341] 17341 17342 17343 17344 17345 17346 17347 17348 17349 17350 17351 17352
## [17353] 17353 17354 17355 17356 17357 17358 17359 17360 17361 17362 17363 17364
## [17365] 17365 17366 17367 17368 17369 17370 17371 17372 17373 17374 17375 17376
## [17377] 17377 17378 17379 17380 17381 17382 17383 17384 17385 17386 17387 17388
## [17389] 17389 17390 17391 17392 17393 17394 17395 17396 17397 17398 17399 17400
## [17401] 17401 17402 17403 17404 17405 17406 17407 17408 17409 17410 17411 17412
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## [17449] 17449 17450 17451 17452 17453 17454 17455 17456 17457 17458 17459 17460
## [17461] 17461 17462 17463 17464 17465 17466 17467 17468 17469 17470 17471 17472
## [17473] 17473 17474 17475 17476 17477 17478 17479 17480 17481 17482 17483 17484
## [17485] 17485 17486 17487 17488 17489 17490 17491 17492 17493 17494 17495 17496
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## [17509] 17509 17510 17511 17512 17513 17514 17515 17516 17517 17518 17519 17520
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## [17569] 17569 17570 17571 17572 17573 17574 17575 17576 17577 17578 17579 17580
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## [17593] 17593 17594 17595 17596 17597 17598 17599 17600 17601 17602 17603 17604
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## [17665] 17665 17666 17667 17668 17669 17670 17671 17672 17673 17674 17675 17676
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## [17689] 17689 17690 17691 17692 17693 17694 17695 17696 17697 17698 17699 17700
## [17701] 17701 17702 17703 17704 17705 17706 17707 17708 17709 17710 17711 17712
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## [17749] 17749 17750 17751 17752 17753 17754 17755 17756 17757 17758 17759 17760
## [17761] 17761 17762 17763 17764 17765 17766 17767 17768 17769 17770 17771 17772
## [17773] 17773 17774 17775 17776 17777 17778 17779 17780 17781 17782 17783 17784
## [17785] 17785 17786 17787 17788 17789 17790 17791 17792 17793 17794 17795 17796
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## [17893] 17893 17894 17895 17896 17897 17898 17899 17900 17901 17902 17903 17904
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## [17965] 17965 17966 17967 17968 17969 17970 17971 17972 17973 17974 17975 17976
## [17977] 17977 17978 17979 17980 17981 17982 17983 17984 17985 17986 17987 17988
## [17989] 17989 17990 17991 17992 17993 17994 17995 17996 17997 17998 17999 18000
## [18001] 18001 18002 18003 18004 18005 18006 18007 18008 18009 18010 18011 18012
## [18013] 18013 18014 18015 18016 18017 18018 18019 18020 18021 18022 18023 18024
## [18025] 18025 18026 18027 18028 18029 18030 18031 18032 18033 18034 18035 18036
## [18037] 18037 18038 18039 18040 18041 18042 18043 18044 18045 18046 18047 18048
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## [18097] 18097 18098 18099 18100 18101 18102 18103 18104 18105 18106 18107 18108
## [18109] 18109 18110 18111 18112 18113 18114 18115 18116
# Then, I correct country and variable names.
Government_Responses$COUNTRY <- Government_Responses$CountryName
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Cape Verde", "Cabo Verde", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Congo", "Congo (Brazzaville)", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Democratic Republic of Congo", "Congo (Kinshasa)", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Myanmar", "Burma", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Czech Republic", "Czechia", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Swaziland", "Eswatini", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Krygz Republic", "Kyrgyzstan", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "South Korea", "Korea, South", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Macedonia", "North Macedonia", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Slovak Republic", "Slovakia", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Macedonia", "North Macedonia", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Taiwan", "Taiwan*", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Timor", "Timor-Leste", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "United States", "US", Government_Responses$COUNTRY)
colnames(Government_Responses)
## [1] "CountryName"
## [2] "CountryCode"
## [3] "Date"
## [4] "C1_School closing"
## [5] "C1_Flag"
## [6] "C1_Notes"
## [7] "C2_Workplace closing"
## [8] "C2_Flag"
## [9] "C2_Notes"
## [10] "C3_Cancel public events"
## [11] "C3_Flag"
## [12] "C3_Notes"
## [13] "C4_Restrictions on gatherings"
## [14] "C4_Flag"
## [15] "C4_Notes"
## [16] "C5_Close public transport"
## [17] "C5_Flag"
## [18] "C5_Notes"
## [19] "C6_Stay at home requirements"
## [20] "C6_Flag"
## [21] "C6_Notes"
## [22] "C7_Restrictions on internal movement"
## [23] "C7_Flag"
## [24] "C7_Notes"
## [25] "C8_International travel controls"
## [26] "C8_Notes"
## [27] "E1_Income support"
## [28] "E1_Flag"
## [29] "E1_Notes"
## [30] "E2_Debt/contract relief"
## [31] "E2_Notes"
## [32] "E3_Fiscal measures"
## [33] "E3_Notes"
## [34] "E4_International support"
## [35] "E4_Notes"
## [36] "H1_Public information campaigns"
## [37] "H1_Flag"
## [38] "H1_Notes"
## [39] "H2_Testing policy"
## [40] "H2_Notes"
## [41] "H3_Contact tracing"
## [42] "H3_Notes"
## [43] "H4_Emergency investment in healthcare"
## [44] "H4_Notes"
## [45] "H5_Investment in vaccines"
## [46] "H5_Notes"
## [47] "M1_Wildcard"
## [48] "M1_Notes"
## [49] "ConfirmedCases"
## [50] "ConfirmedDeaths"
## [51] "StringencyIndex"
## [52] "StringencyIndexForDisplay"
## [53] "LegacyStringencyIndex"
## [54] "LegacyStringencyIndexForDisplay"
## [55] "COUNTRY"
Government_Responses$C1_School.closing <- Government_Responses$`C1_School closing`
Government_Responses$C2_Workplace.closing <- Government_Responses$`C2_Workplace closing`
Government_Responses$C3_Cancel.public.events <- Government_Responses$`C3_Cancel public events`
Government_Responses$C4_Restrictions.on.gatherings <- Government_Responses$`C4_Restrictions on gatherings`
Government_Responses$C5_Close.public.transport <- Government_Responses$`C5_Close public transport`
Government_Responses$C6_Stay.at.home.requirements <- Government_Responses$`C6_Stay at home requirements`
Government_Responses$C7_Restrictions.on.internal.movement <- Government_Responses$`C7_Restrictions on internal movement`
Government_Responses$C8_International.travel.controls <- Government_Responses$`C8_International travel controls`
Government_Responses$E1_Income.support <- Government_Responses$`E1_Income support`
Government_Responses$E2_Debt.contract.relief <- Government_Responses$`E2_Debt/contract relief`
Government_Responses$E3_Fiscal.measures <- Government_Responses$`E3_Fiscal measures`
Government_Responses$E4_International.support <- Government_Responses$`E4_International support`
Government_Responses$H1_Public.information.campaigns <- Government_Responses$`H1_Public information campaigns`
Government_Responses$H2_Testing.policy <- Government_Responses$`H2_Testing policy`
Government_Responses$H3_Contact.tracing <- Government_Responses$`H3_Contact tracing`
Government_Responses$H4_Emergency.investment.in.healthcare <- Government_Responses$`H4_Emergency investment in healthcare`
Government_Responses$H5_Investment.in.vaccines <- Government_Responses$`H5_Investment in vaccines`
# I now subset the data. PLEASE NOTE the observations change in this merge. Oxford began collecting data before Hopkins started tracking cases and deaths. For simplicity, I use the merged data from before to visualize the confirmed cases and deaths (as these dates are filtered correctly).
Government_Responses <- subset(Government_Responses, is.element(Government_Responses$COUNTRY, Temporary_Data_Country$COUNTRY))
# Note that we have two sets of variables for confirmed cases and deaths. The first, I label "Oxford_Cases" and "Oxford_Deaths". The second (from John Hopkins) I label "Total_Cases_Country" and "Total_Deceased_Country". At a later date, we might want to cross-reference these values. Note that Oxford does not provide calculations for province/state; however it is highly likely they used the (same) John Hopkins data.
Government_Responses$Oxford_Cases <- Government_Responses$ConfirmedCases
Government_Responses$Oxford_Deaths <- Government_Responses$ConfirmedDeaths
# Once more, I only keep the variables we are interested in. I elect not to keep the notes... these can be re-added if preferred. E4_International.support also has almost no values above 0, so it is dropped.
colnames(Government_Responses)
## [1] "CountryName"
## [2] "CountryCode"
## [3] "Date"
## [4] "C1_School closing"
## [5] "C1_Flag"
## [6] "C1_Notes"
## [7] "C2_Workplace closing"
## [8] "C2_Flag"
## [9] "C2_Notes"
## [10] "C3_Cancel public events"
## [11] "C3_Flag"
## [12] "C3_Notes"
## [13] "C4_Restrictions on gatherings"
## [14] "C4_Flag"
## [15] "C4_Notes"
## [16] "C5_Close public transport"
## [17] "C5_Flag"
## [18] "C5_Notes"
## [19] "C6_Stay at home requirements"
## [20] "C6_Flag"
## [21] "C6_Notes"
## [22] "C7_Restrictions on internal movement"
## [23] "C7_Flag"
## [24] "C7_Notes"
## [25] "C8_International travel controls"
## [26] "C8_Notes"
## [27] "E1_Income support"
## [28] "E1_Flag"
## [29] "E1_Notes"
## [30] "E2_Debt/contract relief"
## [31] "E2_Notes"
## [32] "E3_Fiscal measures"
## [33] "E3_Notes"
## [34] "E4_International support"
## [35] "E4_Notes"
## [36] "H1_Public information campaigns"
## [37] "H1_Flag"
## [38] "H1_Notes"
## [39] "H2_Testing policy"
## [40] "H2_Notes"
## [41] "H3_Contact tracing"
## [42] "H3_Notes"
## [43] "H4_Emergency investment in healthcare"
## [44] "H4_Notes"
## [45] "H5_Investment in vaccines"
## [46] "H5_Notes"
## [47] "M1_Wildcard"
## [48] "M1_Notes"
## [49] "ConfirmedCases"
## [50] "ConfirmedDeaths"
## [51] "StringencyIndex"
## [52] "StringencyIndexForDisplay"
## [53] "LegacyStringencyIndex"
## [54] "LegacyStringencyIndexForDisplay"
## [55] "COUNTRY"
## [56] "C1_School.closing"
## [57] "C2_Workplace.closing"
## [58] "C3_Cancel.public.events"
## [59] "C4_Restrictions.on.gatherings"
## [60] "C5_Close.public.transport"
## [61] "C6_Stay.at.home.requirements"
## [62] "C7_Restrictions.on.internal.movement"
## [63] "C8_International.travel.controls"
## [64] "E1_Income.support"
## [65] "E2_Debt.contract.relief"
## [66] "E3_Fiscal.measures"
## [67] "E4_International.support"
## [68] "H1_Public.information.campaigns"
## [69] "H2_Testing.policy"
## [70] "H3_Contact.tracing"
## [71] "H4_Emergency.investment.in.healthcare"
## [72] "H5_Investment.in.vaccines"
## [73] "Oxford_Cases"
## [74] "Oxford_Deaths"
myvarsgovernment <- c("COUNTRY", "Date", "C1_School.closing", "C1_Flag", "C2_Workplace.closing", "C2_Flag", "C3_Cancel.public.events", "C3_Flag", "C4_Restrictions.on.gatherings", "C4_Flag", "C5_Close.public.transport", "C5_Flag", "C6_Stay.at.home.requirements", "C6_Flag", "C7_Restrictions.on.internal.movement", "C7_Flag", "C8_International.travel.controls", "E1_Income.support", "E1_Flag", "E2_Debt.contract.relief", "E3_Fiscal.measures", "H1_Public.information.campaigns", "H1_Flag", "H2_Testing.policy", "H3_Contact.tracing", "H4_Emergency.investment.in.healthcare", "H5_Investment.in.vaccines", "M1_Wildcard", "StringencyIndex", "LegacyStringencyIndex", "Oxford_Cases", "Oxford_Deaths")
Government_Responses <- Government_Responses[myvarsgovernment]
# I then merge our data.
Final_Data_Country <- merge(Temporary_Data_Country, Government_Responses, by = c("COUNTRY", "Date"), all=TRUE)
# Next, I specify a set of lagged government response variables for use in our regression later (one-week, two-week, and three-week lags). First, I filter the data and create a lag function for each week (these functions are also used for generating the lags in our testing data).
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# This chunk of code is quite extensive. In our regressions, we use the "lag" function to generate lags for our key variables, rather than using the generated lagged variables below. I simply include them in order to visualize the dataframe with lags incorporated (as the lag function will simply lag existing variables in analysis; not generate new ones to explore). Using this function also allows us to observe which nations do not have enough observations to generate specified lags.
# One Week
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C1_School.closing", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_School_Closing_One_Week <- as.factor(Final_Data_Country$Lagged_School_Closing_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C1_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_General_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_School_Closing_General_One_Week <- as.factor(Final_Data_Country$Lagged_School_Closing_General_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C2_Workplace.closing", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Workplace_Closing_One_Week <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C2_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_General_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Workplace_Closing_General_One_Week <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_General_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C3_Cancel.public.events", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Events_One_Week <- as.factor(Final_Data_Country$Lagged_Public_Events_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C3_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_General_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Events_General_One_Week <- as.factor(Final_Data_Country$Lagged_Public_Events_General_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C4_Restrictions.on.gatherings", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Gatherings_One_Week <- as.factor(Final_Data_Country$Lagged_Gatherings_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C4_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_General_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Gatherings_General_One_Week <- as.factor(Final_Data_Country$Lagged_Gatherings_General_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C5_Close.public.transport", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Transport_One_Week <- as.factor(Final_Data_Country$Lagged_Public_Transport_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C5_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_General_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Transport_General_One_Week <- as.factor(Final_Data_Country$Lagged_Public_Transport_General_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C6_Stay.at.home.requirements", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Lockdown_One_Week <- as.factor(Final_Data_Country$Lagged_Lockdown_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C6_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_General_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Lockdown_General_One_Week <- as.factor(Final_Data_Country$Lagged_Lockdown_General_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C7_Restrictions.on.internal.movement", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_One_Week", keepInvalid =
TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Internal_Movement_One_Week <- as.factor(Final_Data_Country$Lagged_Internal_Movement_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C7_Flag",slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_General_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Internal_Movement_General_One_Week <- as.factor(Final_Data_Country$Lagged_Internal_Movement_General_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C8_International.travel.controls", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_International_Travel_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_International_Travel_One_Week <- as.factor(Final_Data_Country$Lagged_International_Travel_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E1_Income.support", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_One_Week", keepInvalid =
TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Income_Support_One_Week <- as.factor(Final_Data_Country$Lagged_Income_Support_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E1_Flag",slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_General_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Income_Support_General_One_Week <- as.factor(Final_Data_Country$Lagged_Income_Support_General_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E2_Debt.contract.relief", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Debt_Relief_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Debt_Relief_One_Week <- as.factor(Final_Data_Country$Lagged_Debt_Relief_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E3_Fiscal.measures", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Fiscal_Measures_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H1_Public.information.campaigns", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Campaign_One_Week <- as.factor(Final_Data_Country$Lagged_Campaign_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H1_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_General_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Campaign_General_One_Week <- as.factor(Final_Data_Country$Lagged_Campaign_General_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H2_Testing.policy", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Testing_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Testing_One_Week <- as.factor(Final_Data_Country$Lagged_Testing_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H3_Contact.tracing", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Tracing_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Tracing_One_Week <- as.factor(Final_Data_Country$Lagged_Tracing_One_Week)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H4_Emergency.investment.in.healthcare", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H5_Investment.in.vaccines", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_One_Week", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "StringencyIndex", TimeVar = "Date", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Stringency_Index_One_Week", keepInvalid
= TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "LegacyStringencyIndex", TimeVar = "Date", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Legacy_Stringency_Index_One_Week", keepInvalid
= TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 6 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
# Two Weeks
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C1_School.closing", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_School_Closing_Two_Weeks <- as.factor(Final_Data_Country$Lagged_School_Closing_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C1_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_School_Closing_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_School_Closing_General_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C2_Workplace.closing", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Workplace_Closing_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C2_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Workplace_Closing_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_General_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C3_Cancel.public.events", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Events_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Events_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C3_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Events_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Events_General_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C4_Restrictions.on.gatherings", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Gatherings_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Gatherings_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C4_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Gatherings_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Gatherings_General_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C5_Close.public.transport", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Transport_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Transport_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C5_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Transport_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Transport_General_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C6_Stay.at.home.requirements", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Lockdown_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Lockdown_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C6_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Lockdown_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Lockdown_General_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C7_Restrictions.on.internal.movement", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_Two_Weeks", keepInvalid =
TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Internal_Movement_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Internal_Movement_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C7_Flag",slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Internal_Movement_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Internal_Movement_General_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C8_International.travel.controls", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_International_Travel_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_International_Travel_Two_Weeks <- as.factor(Final_Data_Country$Lagged_International_Travel_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E1_Income.support", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_Two_Weeks", keepInvalid =
TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Income_Support_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Income_Support_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E1_Flag",slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Income_Support_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Income_Support_General_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E2_Debt.contract.relief", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Debt_Relief_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Debt_Relief_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Debt_Relief_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E3_Fiscal.measures", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Fiscal_Measures_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H1_Public.information.campaigns", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Campaign_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Campaign_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H1_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Campaign_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Campaign_General_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H2_Testing.policy", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Testing_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Testing_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Testing_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H3_Contact.tracing", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Tracing_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Tracing_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Tracing_Two_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H4_Emergency.investment.in.healthcare", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H5_Investment.in.vaccines", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "StringencyIndex", TimeVar = "Date", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Stringency_Index_Two_Weeks", keepInvalid
= TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "LegacyStringencyIndex", TimeVar = "Date", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Legacy_Stringency_Index_Two_Weeks", keepInvalid
= TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 13 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
# Three Weeks
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C1_School.closing", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_School_Closing_Three_Weeks <- as.factor(Final_Data_Country$Lagged_School_Closing_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C1_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_School_Closing_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_School_Closing_General_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C2_Workplace.closing", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Workplace_Closing_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C2_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Workplace_Closing_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_General_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C3_Cancel.public.events", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Events_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Events_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C3_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Events_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Events_General_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C4_Restrictions.on.gatherings", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Gatherings_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Gatherings_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C4_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Gatherings_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Gatherings_General_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C5_Close.public.transport", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Transport_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Transport_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C5_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Public_Transport_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Transport_General_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C6_Stay.at.home.requirements", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Lockdown_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Lockdown_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C6_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Lockdown_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Lockdown_General_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C7_Restrictions.on.internal.movement", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_Three_Weeks", keepInvalid =
TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Internal_Movement_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Internal_Movement_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C7_Flag",slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Internal_Movement_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Internal_Movement_General_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "C8_International.travel.controls", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_International_Travel_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_International_Travel_Three_Weeks <- as.factor(Final_Data_Country$Lagged_International_Travel_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E1_Income.support", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_Three_Weeks", keepInvalid =
TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Income_Support_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Income_Support_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E1_Flag",slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Income_Support_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Income_Support_General_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E2_Debt.contract.relief", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Debt_Relief_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Debt_Relief_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Debt_Relief_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "E3_Fiscal.measures", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Fiscal_Measures_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H1_Public.information.campaigns", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Campaign_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Campaign_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H1_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Campaign_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Campaign_General_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H2_Testing.policy", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Testing_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Testing_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Testing_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H3_Contact.tracing", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Tracing_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country$Lagged_Tracing_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Tracing_Three_Weeks)
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H4_Emergency.investment.in.healthcare", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "H5_Investment.in.vaccines", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "StringencyIndex", TimeVar = "Date", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Stringency_Index_Three_Weeks", keepInvalid
= TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
slide(Var = "LegacyStringencyIndex", TimeVar = "Date", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Legacy_Stringency_Index_Three_Weeks", keepInvalid
= TRUE, reminder = FALSE)
## Converting to plain data frame from tbl_df.
##
## Warning: the following groups have 20 or fewer observations.
## No valid lag/lead can be created.
## NA will be returned for these observations in the new lag/lead variable.
## They will be returned at the bottom of the data frame.
## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara
##
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# I am using the same Google coordinate data from: https://developers.google.com/public-data/docs/canonical/countries_csv. Note that I edited country names to match the data in the CSV itself (I only kept coordinates for nations in our dataframe).
Country_Coordinates <- read.csv("data/Country_Coordinates.csv")
Country_Coordinates$COUNTRY <- Country_Coordinates$Country
colnames(Country_Coordinates)
## [1] "Latitude" "Longitude" "Country" "COUNTRY"
myvarscoords <- c("COUNTRY", "Latitude", "Longitude")
Country_Coordinates <- Country_Coordinates[myvarscoords]
# I subset the file to only include the nations in our final data.
Country_Coordinates <- subset(Country_Coordinates, is.element(Country_Coordinates$COUNTRY, Final_Data_Country$COUNTRY))
# Lastly, I merge the two dataframes.
Final_Data_Country <- merge(Final_Data_Country, Country_Coordinates, by = c("COUNTRY"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# First, I add the proportion of the national population above 65 from: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS.
pop65yo <- read_csv("data/pop65yo.csv")
## Parsed with column specification:
## cols(
## value = col_double(),
## country = col_character()
## )
pop65yo$COUNTRY <- pop65yo$country
pop65yo$prop65 <- pop65yo$value
# Next, I correct the country names.
pop65yo[19, "COUNTRY"] <- "Bahamas"
pop65yo[26, "COUNTRY"] <- "Brunei"
pop65yo[37, "COUNTRY"] <- "Congo (Kinshasa)"
pop65yo[38, "COUNTRY"] <- "Congo (Brazzaville)"
pop65yo[46, "COUNTRY"] <- "Czechia"
pop65yo[53, "COUNTRY"] <- "Egypt"
pop65yo[66, "COUNTRY"] <- "Gambia"
pop65yo[82, "COUNTRY"] <- "Iran"
pop65yo[95, "COUNTRY"] <- "Korea, South"
pop65yo[92, "COUNTRY"] <- "Kyrgyzstan"
pop65yo[97, "COUNTRY"] <- "Laos"
pop65yo[101, "COUNTRY"] <- "Saint Lucia"
pop65yo[116, "COUNTRY"] <- "Burma"
pop65yo[148, "COUNTRY"] <- "Russia"
pop65yo[162, "COUNTRY"] <- "Slovakia"
pop65yo[167, "COUNTRY"] <- "Syria"
pop65yo[182, "COUNTRY"] <- "US"
pop65yo[184, "COUNTRY"] <- "Saint Vincent and the Grenadines"
pop65yo[185, "COUNTRY"] <- "Venezuela"
pop65yo[186, "COUNTRY"] <- "Virgin Islands"
pop65yo[190, "COUNTRY"] <- "Yemen"
pop65yo[5, "COUNTRY"] <- "United Arab Emirates"
pop65yo[62, "COUNTRY"] <- "United Kingdom"
pop65yo[132, "COUNTRY"] <- "New Zealand"
pop65yo[150, "COUNTRY"] <- "Saudi Arabia"
pop65yo[191, "COUNTRY"] <- "South Africa"
pop65yo <- subset(pop65yo, is.element(pop65yo$COUNTRY, Final_Data_Country$COUNTRY))
# I only keep the columns I care about.
colnames(pop65yo)
## [1] "value" "country" "COUNTRY" "prop65"
myvarspop65yo <- c("prop65", "COUNTRY")
pop65yo <- pop65yo[myvarspop65yo]
pop65yo <- pop65yo[order(pop65yo$COUNTRY, pop65yo$prop65),]
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
Final_Data_Country <- merge(Final_Data_Country, pop65yo, by = c("COUNTRY"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# Now, I add another world development indicator for the proportion of the population which is urban: https://data.worldbank.org/indicator/SP.URB.TOTL.in.zs.
urbanpp <- read_csv("data/urbanpp.csv")
## Parsed with column specification:
## cols(
## propurban = col_double(),
## COUNTRY = col_character()
## )
# Next, I correct the country names.
urbanpp[21, "COUNTRY"] <- "Bahamas"
urbanpp[29, "COUNTRY"] <- "Brunei"
urbanpp[40, "COUNTRY"] <- "Congo (Kinshasa)"
urbanpp[41, "COUNTRY"] <- "Congo (Brazzaville)"
urbanpp[50, "COUNTRY"] <- "Czechia"
urbanpp[58, "COUNTRY"] <- "Egypt"
urbanpp[73, "COUNTRY"] <- "Gambia"
urbanpp[91, "COUNTRY"] <- "Iran"
urbanpp[105, "COUNTRY"] <- "Korea, South"
urbanpp[101, "COUNTRY"] <- "Kyrgyzstan"
urbanpp[107, "COUNTRY"] <- "Laos"
urbanpp[111, "COUNTRY"] <- "Saint Lucia"
urbanpp[129, "COUNTRY"] <- "Burma"
urbanpp[164, "COUNTRY"] <- "Russia"
urbanpp[179, "COUNTRY"] <- "Slovakia"
urbanpp[185, "COUNTRY"] <- "Syria"
urbanpp[202, "COUNTRY"] <- "US"
urbanpp[204, "COUNTRY"] <- "Saint Vincent and the Grenadines"
urbanpp[205, "COUNTRY"] <- "Venezuela"
urbanpp[206, "COUNTRY"] <- "Virgin Islands"
urbanpp[211, "COUNTRY"] <- "Yemen"
urbanpp[6, "COUNTRY"] <- "United Arab Emirates"
urbanpp[68, "COUNTRY"] <- "United Kingdom"
urbanpp[147, "COUNTRY"] <- "New Zealand"
urbanpp[166, "COUNTRY"] <- "Saudi Arabia"
urbanpp[212, "COUNTRY"] <- "South Africa"
urbanpp <- subset(urbanpp, is.element(urbanpp$COUNTRY, Final_Data_Country$COUNTRY))
# I only keep the columns I care about.
colnames(urbanpp)
## [1] "propurban" "COUNTRY"
myvarsurbanpp <- c("propurban", "COUNTRY")
urbanpp <- urbanpp[myvarsurbanpp]
Final_Data_Country <- merge(Final_Data_Country, urbanpp, by = c("COUNTRY"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# Now, I add the population density indicator. Note the latest year available for this data is 2018.
popdensity <- read.csv("data/popdensity.csv")
popdensity$COUNTRY <- popdensity$Country.Name
# Next, I correct the country names.
popdensity[22, "COUNTRY"] <- "Bahamas"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated
popdensity[30, "COUNTRY"] <- "Brunei"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated
popdensity[42, "COUNTRY"] <- "Congo (Kinshasa)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated
popdensity[43, "COUNTRY"] <- "Congo (Brazzaville)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated
popdensity[53, "COUNTRY"] <- "Czechia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated
popdensity[66, "COUNTRY"] <- "Egypt"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated
popdensity[85, "COUNTRY"] <- "Gambia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated
popdensity[111, "COUNTRY"] <- "Iran"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated
popdensity[125, "COUNTRY"] <- "Korea, South"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated
popdensity[121, "COUNTRY"] <- "Kyrgyzstan"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated
popdensity[128, "COUNTRY"] <- "Laos"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated
popdensity[132, "COUNTRY"] <- "Saint Lucia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated
popdensity[159, "COUNTRY"] <- "Burma"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated
popdensity[201, "COUNTRY"] <- "Russia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated
popdensity[220, "COUNTRY"] <- "Slovakia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated
popdensity[226, "COUNTRY"] <- "Syria"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated
popdensity[250, "COUNTRY"] <- "US"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated
popdensity[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated
popdensity[253, "COUNTRY"] <- "Venezuela"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated
popdensity[255, "COUNTRY"] <- "Virgin Islands"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated
popdensity[261, "COUNTRY"] <- "Yemen"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated
popdensity[7, "COUNTRY"] <- "United Arab Emirates"
popdensity[80, "COUNTRY"] <- "United Kingdom"
popdensity[179, "COUNTRY"] <- "New Zealand"
popdensity[204, "COUNTRY"] <- "Saudi Arabia"
popdensity[262, "COUNTRY"] <- "South Africa"
popdensity <- subset(popdensity, COUNTRY %in% Final_Data_Country$COUNTRY)
# I only keep the columns I care about.
colnames(popdensity)
## [1] "Country.Name" "popdensity" "COUNTRY"
myvarspopdensity <- c("popdensity", "COUNTRY")
popdensity <- popdensity[myvarspopdensity]
popdensity <- popdensity[order(popdensity$COUNTRY, popdensity$popdensity),]
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
Final_Data_Country <- merge(Final_Data_Country, popdensity, by = c("COUNTRY"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
***** This has been commented out for now, because of the poor quality of the data. However, the dataframe is updated with the newest results each time the chunk is run. If we want to re-implement testing in some way, we can uncomment and run this chunk. *****
# Next, I add our testing data from:
# owurl = "https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/testing/covid-testing-all-observations.csv"
# testcap <- read_csv(url(owurl))
# testcap <- separate(testcap, Entity, into=c("Country", "Unit type"), sep = " - ")
# write_csv(testcap, path = "/Users/sameernair-desai/Desktop/data/testcap.csv")
# testcap$COUNTRY <- testcap$Country
# testcap$COUNTRY <- ifelse(testcap$COUNTRY == "Czech Republic", "Czechia", testcap$COUNTRY)
# testcap$COUNTRY <- ifelse(testcap$COUNTRY == "South Korea", "Korea, South", testcap$COUNTRY)
# testcap$COUNTRY <- ifelse(testcap$COUNTRY == "Taiwan", "Taiwan*", testcap$COUNTRY)
# testcap$COUNTRY <- ifelse(testcap$COUNTRY == "United States", "US", testcap$COUNTRY)
# testcap <- subset(testcap, is.element(testcap$COUNTRY, Final_Data_Country$COUNTRY))
# I don't like some of the variable names in the original data, so I change them below.
# testcap$Test_Type <- testcap$`Unit type`
# testcap$Cumulative_Tests <- testcap$`Cumulative total`
# testcap$Daily_Change_Cumulative_Tests <- testcap$`Daily change in cumulative total`
# testcap$Cumulative_Tests_Per_Thousand <- testcap$`Cumulative total per thousand`
# testcap$Daily_Change_Cumulative_Tests_Per_Thousand <- testcap$`Daily change in cumulative total per thousand`
# testcap$Three_Day_Rolling_Mean_Tests <- testcap$`3-day rolling mean daily change`
# testcap$Three_Day_Rolling_Mean_Tests_Per_Thousand <- testcap$`3-day rolling mean daily change per thousand`
#
# Here, I keep the variables I care about.
# colnames(testcap)
# myvarstest <- c("COUNTRY", "Date", "Test_Type", "Cumulative_Tests", "Daily_Change_Cumulative_Tests", "Cumulative_Tests_Per_Thousand", "Daily_Change_Cumulative_Tests_Per_Thousand", "Three_Day_Rolling_Mean_Tests", "Three_Day_Rolling_Mean_Tests_Per_Thousand")
# testcap <- testcap[myvarstest]
# Now, I convert from long to wide format.
# testcap <- pivot_wider(testcap, id_cols = c("COUNTRY", "Date", "Test_Type"), names_from = "Test_Type", values_from = c("Cumulative_Tests", "Daily_Change_Cumulative_Tests", "Cumulative_Tests_Per_Thousand", "Daily_Change_Cumulative_Tests_Per_Thousand", "Three_Day_Rolling_Mean_Tests", "Three_Day_Rolling_Mean_Tests_Per_Thousand"))
# Now, I merge the data to our final file.
# Final_Data_Country <- merge(Final_Data_Country, testcap, by = c("COUNTRY", "Date"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# Here, I integrate the Apple Mobility data.
mobility <- read.csv("data/applemobilitytrends-2020-04-27.csv")
# First, we convert from wide to long format.
mobility <- pivot_longer(mobility, cols = starts_with("X"), names_to = "Dates")
# Next, we reformat the dates.
mobility$Dates<-sub(".", "", mobility$Dates)
mobility$Date <- mdy(mobility$Dates, quiet = FALSE, tz = NULL, locale = Sys.getlocale("LC_TIME"),
truncated = 0)
# I only keep the variables we care about. I also filter for only countries (we can re-add cities at a later period).
mobility <- mobility %>%
dplyr:: filter(mobility$geo_type == "country/region")
colnames(mobility)
## [1] "geo_type" "region" "transportation_type"
## [4] "alternative_name" "Dates" "value"
## [7] "Date"
myvarsmobility <- c("region", "transportation_type", "Date", "value")
mobility <- mobility[myvarsmobility]
mobility$COUNTRY <- mobility$region
# Now, I re-convert from long to wide format.
mobility <- pivot_wider(mobility, id_cols = c("COUNTRY", "Date", "transportation_type"), names_from = "transportation_type", values_from = c("value"))
# Next, we ensure the countries from the mobility file and our data align.
mobility$COUNTRY <- ifelse(mobility$COUNTRY == "Czech Republic", "Czechia", mobility$COUNTRY)
mobility$COUNTRY <- ifelse(mobility$COUNTRY == "Republic of Korea", "Korea, South", mobility$COUNTRY)
mobility$COUNTRY <- ifelse(mobility$COUNTRY == "Taiwan", "Taiwan*", mobility$COUNTRY)
mobility$COUNTRY <- ifelse(mobility$COUNTRY == "UK", "United Kingdom", mobility$COUNTRY)
mobility$COUNTRY <- ifelse(mobility$COUNTRY == "United States", "US", mobility$COUNTRY)
mobility <- subset(mobility, is.element(mobility$COUNTRY, Final_Data_Country$COUNTRY))
Final_Data_Country <- merge(Final_Data_Country, mobility, by = c("COUNTRY", "Date"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# First, I add information on the number of tourist arrivals (2018 is l.y.a.). Run this in the console
arrivals <- read.csv("data/arrivals.csv")
arrivals$COUNTRY <- arrivals$Country.Name
# Next, I correct the country names.
arrivals[22, "COUNTRY"] <- "Bahamas"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated
arrivals[30, "COUNTRY"] <- "Brunei"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated
arrivals[42, "COUNTRY"] <- "Congo (Kinshasa)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated
arrivals[43, "COUNTRY"] <- "Congo (Brazzaville)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated
arrivals[53, "COUNTRY"] <- "Czechia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated
arrivals[66, "COUNTRY"] <- "Egypt"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated
arrivals[85, "COUNTRY"] <- "Gambia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated
arrivals[111, "COUNTRY"] <- "Iran"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated
arrivals[125, "COUNTRY"] <- "Korea, South"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated
arrivals[121, "COUNTRY"] <- "Kyrgyzstan"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated
arrivals[128, "COUNTRY"] <- "Laos"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated
arrivals[132, "COUNTRY"] <- "Saint Lucia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated
arrivals[159, "COUNTRY"] <- "Burma"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated
arrivals[201, "COUNTRY"] <- "Russia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated
arrivals[220, "COUNTRY"] <- "Slovakia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated
arrivals[226, "COUNTRY"] <- "Syria"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated
arrivals[250, "COUNTRY"] <- "US"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated
arrivals[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated
arrivals[253, "COUNTRY"] <- "Venezuela"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated
arrivals[255, "COUNTRY"] <- "Virgin Islands"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated
arrivals[261, "COUNTRY"] <- "Yemen"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated
arrivals[7, "COUNTRY"] <- "United Arab Emirates"
arrivals[80, "COUNTRY"] <- "United Kingdom"
arrivals[179, "COUNTRY"] <- "New Zealand"
arrivals[204, "COUNTRY"] <- "Saudi Arabia"
arrivals[262, "COUNTRY"] <- "South Africa"
arrivals <- subset(arrivals, COUNTRY %in% Final_Data_Country$COUNTRY)
# I only keep the columns I care about.
colnames(arrivals)
## [1] "Country.Name" "Country.Code" "arrivals" "COUNTRY"
myvarsarrivals <- c("arrivals", "COUNTRY")
arrivals <- arrivals[myvarsarrivals]
Final_Data_Country <- merge(Final_Data_Country, arrivals, by = c("COUNTRY"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# Next, I add information on tourism departures (2018 is l.y.a.).
departures <- read.csv("data/departures.csv")
departures$COUNTRY <- departures$Country.Name
# Next, I correct the country names.
departures[22, "COUNTRY"] <- "Bahamas"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated
departures[30, "COUNTRY"] <- "Brunei"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated
departures[42, "COUNTRY"] <- "Congo (Kinshasa)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated
departures[43, "COUNTRY"] <- "Congo (Brazzaville)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated
departures[53, "COUNTRY"] <- "Czechia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated
departures[66, "COUNTRY"] <- "Egypt"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated
departures[85, "COUNTRY"] <- "Gambia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated
departures[111, "COUNTRY"] <- "Iran"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated
departures[125, "COUNTRY"] <- "Korea, South"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated
departures[121, "COUNTRY"] <- "Kyrgyzstan"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated
departures[128, "COUNTRY"] <- "Laos"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated
departures[132, "COUNTRY"] <- "Saint Lucia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated
departures[159, "COUNTRY"] <- "Burma"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated
departures[201, "COUNTRY"] <- "Russia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated
departures[220, "COUNTRY"] <- "Slovakia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated
departures[226, "COUNTRY"] <- "Syria"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated
departures[250, "COUNTRY"] <- "US"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated
departures[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated
departures[253, "COUNTRY"] <- "Venezuela"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated
departures[255, "COUNTRY"] <- "Virgin Islands"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated
departures[261, "COUNTRY"] <- "Yemen"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated
departures[7, "COUNTRY"] <- "United Arab Emirates"
departures[80, "COUNTRY"] <- "United Kingdom"
departures[179, "COUNTRY"] <- "New Zealand"
departures[204, "COUNTRY"] <- "Saudi Arabia"
departures[262, "COUNTRY"] <- "South Africa"
departures <- subset(departures, COUNTRY %in% Final_Data_Country$COUNTRY)
# I only keep the columns I care about.
colnames(departures)
## [1] "Country.Name" "departures" "COUNTRY"
myvarsdepartures <- c("departures", "COUNTRY")
departures <- departures[myvarsdepartures]
Final_Data_Country <- merge(Final_Data_Country, departures, by = c("COUNTRY"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# First, I add the percentage of vulnerable employment by nation from: https://data.worldbank.org/indicator/SL.EMP.VULN.ZS?view=chart
vulnerable_employment <- read.csv("data/vulnerable_employment.csv")
# Next, I correct the country names.
vulnerable_employment[22, "COUNTRY"] <- "Bahamas"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated
vulnerable_employment[30, "COUNTRY"] <- "Brunei"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated
vulnerable_employment[42, "COUNTRY"] <- "Congo (Kinshasa)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated
vulnerable_employment[43, "COUNTRY"] <- "Congo (Brazzaville)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated
vulnerable_employment[53, "COUNTRY"] <- "Czechia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated
vulnerable_employment[66, "COUNTRY"] <- "Egypt"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated
vulnerable_employment[85, "COUNTRY"] <- "Gambia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated
vulnerable_employment[111, "COUNTRY"] <- "Iran"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated
vulnerable_employment[125, "COUNTRY"] <- "Korea, South"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated
vulnerable_employment[121, "COUNTRY"] <- "Kyrgyzstan"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated
vulnerable_employment[128, "COUNTRY"] <- "Laos"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated
vulnerable_employment[132, "COUNTRY"] <- "Saint Lucia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated
vulnerable_employment[159, "COUNTRY"] <- "Burma"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated
vulnerable_employment[201, "COUNTRY"] <- "Russia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated
vulnerable_employment[220, "COUNTRY"] <- "Slovakia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated
vulnerable_employment[226, "COUNTRY"] <- "Syria"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated
vulnerable_employment[250, "COUNTRY"] <- "US"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated
vulnerable_employment[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated
vulnerable_employment[253, "COUNTRY"] <- "Venezuela"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated
vulnerable_employment[255, "COUNTRY"] <- "Virgin Islands"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated
vulnerable_employment[261, "COUNTRY"] <- "Yemen"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated
vulnerable_employment[7, "COUNTRY"] <- "United Arab Emirates"
vulnerable_employment[80, "COUNTRY"] <- "United Kingdom"
vulnerable_employment[179, "COUNTRY"] <- "New Zealand"
vulnerable_employment[204, "COUNTRY"] <- "Saudi Arabia"
vulnerable_employment[262, "COUNTRY"] <- "South Africa"
vulnerable_employment <- subset(vulnerable_employment, COUNTRY %in% Final_Data_Country$COUNTRY)
# I only keep the columns I care about.
colnames(vulnerable_employment)
## [1] "COUNTRY" "vul_emp"
myvarsvulnerable_employment <- c("vul_emp", "COUNTRY")
vulnerable_employment <- vulnerable_employment[myvarsvulnerable_employment]
Final_Data_Country <- merge(Final_Data_Country, vulnerable_employment, by = c("COUNTRY"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# Next, I add GNI per capita from: https://data.worldbank.org/indicator/NY.GNP.PCAP.CD?view=chart.
GNI <- read.csv("data/GNI.csv")
# Next, I correct the country names.
GNI[22, "COUNTRY"] <- "Bahamas"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated
GNI[30, "COUNTRY"] <- "Brunei"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated
GNI[42, "COUNTRY"] <- "Congo (Kinshasa)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated
GNI[43, "COUNTRY"] <- "Congo (Brazzaville)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated
GNI[53, "COUNTRY"] <- "Czechia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated
GNI[66, "COUNTRY"] <- "Egypt"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated
GNI[85, "COUNTRY"] <- "Gambia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated
GNI[111, "COUNTRY"] <- "Iran"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated
GNI[125, "COUNTRY"] <- "Korea, South"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated
GNI[121, "COUNTRY"] <- "Kyrgyzstan"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated
GNI[128, "COUNTRY"] <- "Laos"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated
GNI[132, "COUNTRY"] <- "Saint Lucia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated
GNI[159, "COUNTRY"] <- "Burma"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated
GNI[201, "COUNTRY"] <- "Russia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated
GNI[220, "COUNTRY"] <- "Slovakia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated
GNI[226, "COUNTRY"] <- "Syria"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated
GNI[250, "COUNTRY"] <- "US"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated
GNI[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated
GNI[253, "COUNTRY"] <- "Venezuela"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated
GNI[255, "COUNTRY"] <- "Virgin Islands"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated
GNI[261, "COUNTRY"] <- "Yemen"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated
GNI[7, "COUNTRY"] <- "United Arab Emirates"
GNI[80, "COUNTRY"] <- "United Kingdom"
GNI[179, "COUNTRY"] <- "New Zealand"
GNI[204, "COUNTRY"] <- "Saudi Arabia"
GNI[262, "COUNTRY"] <- "South Africa"
GNI <- subset(GNI, COUNTRY %in% Final_Data_Country$COUNTRY)
# I only keep the columns I care about.
colnames(GNI)
## [1] "COUNTRY" "GNI"
myvarsGNI <- c("GNI", "COUNTRY")
GNI <- GNI[myvarsGNI]
Final_Data_Country <- merge(Final_Data_Country, GNI, by = c("COUNTRY"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# Next, I control for current health expenditures (l.y.a. is 2017): https://data.worldbank.org/indicator/SH.XPD.CHEX.PC.CD?view=chart.
health_pc <- read.csv("data/health_pc.csv")
# Next, I correct the country names.
health_pc[22, "COUNTRY"] <- "Bahamas"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated
health_pc[30, "COUNTRY"] <- "Brunei"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated
health_pc[42, "COUNTRY"] <- "Congo (Kinshasa)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated
health_pc[43, "COUNTRY"] <- "Congo (Brazzaville)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated
health_pc[53, "COUNTRY"] <- "Czechia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated
health_pc[66, "COUNTRY"] <- "Egypt"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated
health_pc[85, "COUNTRY"] <- "Gambia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated
health_pc[111, "COUNTRY"] <- "Iran"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated
health_pc[125, "COUNTRY"] <- "Korea, South"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated
health_pc[121, "COUNTRY"] <- "Kyrgyzstan"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated
health_pc[128, "COUNTRY"] <- "Laos"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated
health_pc[132, "COUNTRY"] <- "Saint Lucia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated
health_pc[159, "COUNTRY"] <- "Burma"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated
health_pc[201, "COUNTRY"] <- "Russia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated
health_pc[220, "COUNTRY"] <- "Slovakia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated
health_pc[226, "COUNTRY"] <- "Syria"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated
health_pc[250, "COUNTRY"] <- "US"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated
health_pc[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated
health_pc[253, "COUNTRY"] <- "Venezuela"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated
health_pc[255, "COUNTRY"] <- "Virgin Islands"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated
health_pc[261, "COUNTRY"] <- "Yemen"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated
health_pc[7, "COUNTRY"] <- "United Arab Emirates"
health_pc[80, "COUNTRY"] <- "United Kingdom"
health_pc[179, "COUNTRY"] <- "New Zealand"
health_pc[204, "COUNTRY"] <- "Saudi Arabia"
health_pc[262, "COUNTRY"] <- "South Africa"
health_pc <- subset(health_pc, COUNTRY %in% Final_Data_Country$COUNTRY)
# I only keep the columns I care about.
colnames(health_pc)
## [1] "COUNTRY" "health_exp"
myvarshealth_pc <- c("health_exp", "COUNTRY")
health_pc <- health_pc[myvarshealth_pc]
Final_Data_Country <- merge(Final_Data_Country, health_pc, by = c("COUNTRY"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# I also add data on air pollution by nation from: https://data.worldbank.org/indicator/EN.ATM.PM25.MC.M3?view=chart.
pollution <- read.csv("data/pollution.csv")
# Next, I correct the country names.
pollution[22, "COUNTRY"] <- "Bahamas"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated
pollution[30, "COUNTRY"] <- "Brunei"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated
pollution[42, "COUNTRY"] <- "Congo (Kinshasa)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated
pollution[43, "COUNTRY"] <- "Congo (Brazzaville)"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated
pollution[53, "COUNTRY"] <- "Czechia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated
pollution[66, "COUNTRY"] <- "Egypt"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated
pollution[85, "COUNTRY"] <- "Gambia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated
pollution[111, "COUNTRY"] <- "Iran"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated
pollution[125, "COUNTRY"] <- "Korea, South"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated
pollution[121, "COUNTRY"] <- "Kyrgyzstan"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated
pollution[128, "COUNTRY"] <- "Laos"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated
pollution[132, "COUNTRY"] <- "Saint Lucia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated
pollution[159, "COUNTRY"] <- "Burma"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated
pollution[201, "COUNTRY"] <- "Russia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated
pollution[220, "COUNTRY"] <- "Slovakia"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated
pollution[226, "COUNTRY"] <- "Syria"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated
pollution[250, "COUNTRY"] <- "US"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated
pollution[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated
pollution[253, "COUNTRY"] <- "Venezuela"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated
pollution[255, "COUNTRY"] <- "Virgin Islands"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated
pollution[261, "COUNTRY"] <- "Yemen"
## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated
pollution[7, "COUNTRY"] <- "United Arab Emirates"
pollution[80, "COUNTRY"] <- "United Kingdom"
pollution[179, "COUNTRY"] <- "New Zealand"
pollution[204, "COUNTRY"] <- "Saudi Arabia"
pollution[262, "COUNTRY"] <- "South Africa"
pollution <- subset(pollution, COUNTRY %in% Final_Data_Country$COUNTRY)
# I only keep the columns I care about.
colnames(pollution)
## [1] "COUNTRY" "pollution"
myvarspollution <- c("pollution", "COUNTRY")
pollution <- pollution[myvarspollution]
Final_Data_Country <- merge(Final_Data_Country, pollution, by = c("COUNTRY"), all=TRUE)
# Here, I add democracy indicators from Freedom House: https://freedomhouse.org/countries/freedom-world/scores and the Economist: https://www.eiu.com/topic/democracy-index?&zid=democracyindex2019&utm_source=blog&utm_medium=blog&utm_name=democracyindex2019&utm_term=democracyindex2019&utm_content=top_link.
democracy <- read.csv("data/democracy.csv")
democracy <- subset(democracy, COUNTRY %in% Final_Data_Country$COUNTRY)
# I only keep the columns I care about.
colnames(democracy)
## [1] "COUNTRY" "EUI_democracy" "freedom_house"
myvarsdemocracy <- c("EUI_democracy", "freedom_house", "COUNTRY")
democracy <- democracy[myvarsdemocracy]
Final_Data_Country <- merge(Final_Data_Country, democracy, by = c("COUNTRY"), all=TRUE)
# Here, I add data on the number of cellular subscriptions by nation from: https://data.worldbank.org/indicator/IT.CEL.SETS.P2?start=1960.
cellular <- read.csv("data/cellular.csv")
cellular <- subset(cellular, COUNTRY %in% Final_Data_Country$COUNTRY)
# I only keep the columns I care about.
colnames(cellular)
## [1] "COUNTRY" "cellular_sub"
myvarscellular <- c("cellular_sub", "COUNTRY")
cellular <- cellular[myvarscellular]
Final_Data_Country <- merge(Final_Data_Country, cellular, by = c("COUNTRY"), all=TRUE)
# Now, I add military data from: https://correlatesofwar.org/data-sets/national-material-capabilities. Note the l.y.a. is 2012.
military <- read.csv("data/military.csv")
military <- subset(military, COUNTRY %in% Final_Data_Country$COUNTRY)
# I only keep the columns I care about.
colnames(military)
## [1] "COUNTRY" "milex" "milper" "irst" "pec" "cinc"
myvarsmilitary <- c("milex", "milper", "irst", "pec", "cinc", "COUNTRY")
military <- military[myvarsmilitary]
Final_Data_Country <- merge(Final_Data_Country, military, by = c("COUNTRY"), all=TRUE)
# I now add data on diseases (aggregated from 2015-2018) from the WHO ICD10: https://www.who.int/classifications/icd/icdonlineversions/en/.
diseases <- read.csv("data/ICD_Deaths_Final.csv")
diseases <- subset(diseases, COUNTRY %in% Final_Data_Country$COUNTRY)
Final_Data_Country <- merge(Final_Data_Country, diseases, by = c("COUNTRY"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# Labelling our variables.
label(Final_Data_Country$C1_School.closing) <- "Record Closings of Schools and Universities (0 - No Measures; 1 - Recommended Closing; 2 - Required Closing (some levels); 3 - Required Closing (all levels))"
label(Final_Data_Country$C1_Flag) <- "Targeted vs. General School Closings (0 - Targeted; 1 - General)"
label(Final_Data_Country$C2_Workplace.closing) <- "Record Closings of Workplaces (0 - No Measures; 1 - Recommended Closing; 2 - Required Closing (some sectors); 3 - Required Closing (all but essential workers))"
label(Final_Data_Country$C2_Flag) <- "Targeted vs. General Workplace Closings (0 - Targeted; 1 - General)"
label(Final_Data_Country$C3_Cancel.public.events) <- "Record Closings of Public Events (0 - No Measures; 1 - Recommended Cancelling; 2 - Required Cancelling)"
label(Final_Data_Country$C3_Flag) <- "Targeted vs. General Public Event Closings (0 - Targeted; 1 - General)"
label(Final_Data_Country$C4_Restrictions.on.gatherings) <- "Restrictions on Public Gatherings (0 - No Measures; 1 - Restrictions on Gatherings of 1000 + people; 2 - Restrictions on Gatherings of 100-1000 people; 3 - Restrictions on Gatherings of 10-100 people; 4 - Restrictions on Gatherings of < 10 people)"
label(Final_Data_Country$C4_Flag) <- "Targeted vs. General Public Gathering Restrictions (0 - Targeted; 1 - General)"
label(Final_Data_Country$C5_Close.public.transport) <- "Record Closings of Public Transport (0 - No Measures; 1 - Recommended Closing (means of transport reduced); 2 - Required Closing (prohibit public transit operations))"
label(Final_Data_Country$C5_Flag) <- "Targeted vs. General Public Transport Closings (0 - Targeted; 1 - General)"
label(Final_Data_Country$C6_Stay.at.home.requirements) <- "Lockdown Measures (0 - No Measures; 1 - Recommendeded; 2 - Lax Lockdown (allows for exercise, shopping, and essential trips); 3 - Strict Lockdown)"
label(Final_Data_Country$C6_Flag) <- "Targeted vs. General Lockdown (0 - Targeted; 1 - General)"
label(Final_Data_Country$C7_Restrictions.on.internal.movement) <- "Record Restrictions on Internal Movement (0 - No Measures; 1 - Recommend Movement Restriction (reduce means of transport); 2 - Restrict Movement (prohibit internal movement))"
label(Final_Data_Country$C7_Flag) <- "Targeted vs. General Restrictions on Internal Movement (0 - Targeted; 1 - General)"
label(Final_Data_Country$C8_International.travel.controls) <- "Record Restrictions on International Travel (0 - No Measures; 1 - Screening; 2 - Quarantine on High-Risk Regions; 3 - Ban on High-Risk Regions)"
label(Final_Data_Country$E1_Income.support) <- "Income Support (0 - None; 1 - Govt. Funds < 50% of Lost Salary; 2 - Govt. Funds > 50% of Lost Salary)"
label(Final_Data_Country$E1_Flag) <- "Targeted vs. General Income Support"
label(Final_Data_Country$E2_Debt.contract.relief) <- "Debt Relief (0 - None; 1 - Narrow Relief (specific kinds of contracts); 2 - Broad Relief)"
label(Final_Data_Country$E3_Fiscal.measures) <- "Value of Fiscal Stimuli, Including Spending or Tax Cuts (in USD)"
label(Final_Data_Country$H1_Public.information.campaigns) <- "Record Presence of Public Information Campaigns (0 - No; 1 - Public Officials Urge Caution; 2 - Coordinated Public Information Campaign)"
label(Final_Data_Country$H1_Flag) <- "Targeted vs. General Public Information Campaigns (0 - Targeted; 1 - General)"
label(Final_Data_Country$H2_Testing.policy) <- "Who Can Get Tested (0 - No Testing Policy; 1 - Testing for those with a) Symptoms AND b) Meet Given Criteria (key workers; admitted to hospital; came into contact with known patient; returned from overseas); 2 - Testing of anyone showing symptoms; 3 - Open Public Testing ('drive-through testing')"
label(Final_Data_Country$H3_Contact.tracing) <- "Contact Tracing (0 - No Contact Tracing; 1 - Limited Contact Tracing; 2 - Comprehensive Contact Tracing)"
label(Final_Data_Country$H4_Emergency.investment.in.healthcare) <- "Value of New Short-Term Spending on Health (in USD)"
label(Final_Data_Country$H5_Investment.in.vaccines) <- "Value of Investment (in USD)"
label(Final_Data_Country$StringencyIndex) <- "Index for Government Stringency (calculated using C1-S8 + H1)"
label(Final_Data_Country$LegacyStringencyIndex) <- "Legacy Index for Government Stringency (converts new calculation to prior calculation for our 'original' Stringency Index)"
label(Final_Data_Country$Oxford_Cases) <- "Cumulative Sum of Confirmed Cases by Country (Oxford)"
label(Final_Data_Country$Oxford_Deaths) <- "Cumulative Sum of Deaths by Country (Oxford)"
label(Final_Data_Country$prop65) <- "Proportion of the Population Above Age 65"
label(Final_Data_Country$propurban) <- "Proportion of Population in Urban Areas"
label(Final_Data_Country$popdensity) <- "People per 100 Sq. Km. of Land Area"
label(Final_Data_Country$arrivals) <- "Number of Tourist Arrivals"
label(Final_Data_Country$departures) <- "Number of Tourist Departures"
label(Final_Data_Country$vul_emp) <- "Percentage of Population in Vulnerable Employment"
label(Final_Data_Country$GNI) <- "GNI per capita (current USD - Atlas Method) "
label(Final_Data_Country$health_exp) <- "Health Expenditure Per Capita"
label(Final_Data_Country$pollution) <- "PM2.5 Air Pollution (micrograms per cubic meter)"
label(Final_Data_Country$EUI_democracy) <- "Democracy scores from the Economist Intelligence Unit"
label(Final_Data_Country$freedom_house) <- "Democracy scores from Freedom House"
label(Final_Data_Country$milex) <- "Military expenditures (thousands of GBP)"
label(Final_Data_Country$milper) <- "Military personnel (thousands of soldiers)"
label(Final_Data_Country$irst) <- "Iron and steel production (thousands of ton)"
label(Final_Data_Country$pec) <- "Primary energy consumption (thousands of coal-ton equivalents)"
label(Final_Data_Country$cinc) <- "Composite Index of National Capability score"
# For now, we are restricting our analysis to OECD/EM markets. I do so below, using a loose definition of EMs from: https://www.msci.com/documents/10199/c0db0a48-01f2-4ba9-ad01-226fd5678111 and advanced economies from: https://www.imf.org/external/pubs/ft/weo/2020/01/weodata/groups.htm. Provinces of China (Taiwan, Macao, Hong Kong) and Italy (San Marino) are aggregated to the national level. Note that Lithuania, while included here for visualizations, does not have government responses data and is thus not run in the regressions.
Final_Data_Country <- subset(Final_Data_Country, (COUNTRY == "Argentina" | COUNTRY == "Australia" | COUNTRY == "Austria" | COUNTRY == "Belgium" | COUNTRY == "Brazil" | COUNTRY == "Canada" | COUNTRY == "Chile" | COUNTRY == "China" | COUNTRY == "Colombia" | COUNTRY == "Cyprus" | COUNTRY == "Czechia" |COUNTRY == "Denmark" | COUNTRY == "Egypt" | COUNTRY == "Estonia" | COUNTRY == "Finland" | COUNTRY == "France" | COUNTRY == "Germany" | COUNTRY == "Greece" | COUNTRY == "Hungary" | COUNTRY == "Iceland" | COUNTRY == "India" | COUNTRY == "Indonesia" | COUNTRY == "Ireland" | COUNTRY == "Israel" | COUNTRY == "Italy" | COUNTRY == "Japan" | COUNTRY == "Jordan" | COUNTRY == "Korea, South" | COUNTRY == "Kuwait" | COUNTRY == "Latvia" | COUNTRY == "Lithuania" | COUNTRY == "Luxembourg" | COUNTRY == "Malaysia" | COUNTRY == "Malta" | COUNTRY == "Mexico" | COUNTRY == "Morocco" | COUNTRY == "Netherlands" | COUNTRY == "New Zealand" |COUNTRY == "Norway" | COUNTRY == "Peru" | COUNTRY == "Philippines" | COUNTRY == "Poland" | COUNTRY == "Portugal" | COUNTRY == "Qatar" | COUNTRY == "Russia" | COUNTRY == "Saudi Arabia" | COUNTRY == "Singapore" | COUNTRY == "Slovakia" | COUNTRY == "Slovenia" | COUNTRY == "South Africa" | COUNTRY == "Spain" | COUNTRY == "Sweden" | COUNTRY == "Switzerland" | COUNTRY == "Thailand" | COUNTRY == "Turkey" | COUNTRY == "United Arab Emirates" | COUNTRY == "United Kingdom" | COUNTRY == "US" | COUNTRY == "Vietnam"))
# Next, I label and clean the final dataset. This is excluded for brevity.
remove(Final_Data_Country)
# Note that I change the column types so reader can properly parse the data. This dataset also includes lagged variables for the mortality rate, which are not generated in the code for brevity.
library(readr)
Final_Data_Country <- read_csv(("data/Final_Data_Country.csv"),
col_types = cols(`E2_Debt.contract.relief` = col_number(),
`Lagged_Debt_Relief_One_Week` = col_number(),
`Lagged_Debt_Relief_Two_Weeks` = col_number(),
`Lagged_Debt_Relief_Three_Weeks` = col_number(),
`E1_Income.support` = col_number(),
`Lagged_Income_Support_One_Week` = col_number(),
`Lagged_Income_Support_Two_Weeks` = col_number(),
`Lagged_Income_Support_Three_Weeks` = col_number()))
## Warning: Missing column names filled in: 'X1' [1]
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# I now add data on government indicators from: https://info.worldbank.org/governance/wgi/Home/Documents.
govt <- read.csv("data/govt.csv")
govt <- subset(govt, COUNTRY %in% Final_Data_Country$COUNTRY)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
Final_Data_Country <- merge(Final_Data_Country, govt, by = c("COUNTRY"), all=TRUE)
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
# Now, we want to integrate our spatial datasets from CEPII.
geo_cepii <- read_csv("data/spatial_data/geo_cepii.csv")
## Parsed with column specification:
## cols(
## .default = col_character(),
## cnum = col_double(),
## area = col_double(),
## dis_int = col_double(),
## landlocked = col_double(),
## lat = col_double(),
## lon = col_double(),
## cap = col_double(),
## maincity = col_double(),
## citynum = col_double()
## )
## See spec(...) for full column specifications.
# We only have country-level information for our COVID data. So, we want to collapse the geo_cepii data to the country level, without losing the local variation provided by our coordinates and distances for city indicators. However, in the case where we have multiple cities per country, we must make a decision on how to collapse. I choose to only keep the capital cities, and collapse by countries in this way.
geo_cepii_final <- geo_cepii %>%
dplyr:: filter(geo_cepii$cap == 1)
geo_cepii_final$COUNTRY <- geo_cepii_final$country
geo_cepii_final$City <- geo_cepii_final$city_en
geo_cepii_final$City_Latitude <- geo_cepii_final$lat
geo_cepii_final$City_Longitude <- geo_cepii_final$lon
geo_cepii_final$Capital <- geo_cepii_final$cap
# Below, I create a new dataframe for merging the geo_cepii data with the distance_cepii data.
geo_cepii_merge <- geo_cepii_final[c(2,35)]
# Now, I read in the distance_cepii data.
distance_cepii <- read_csv("data/spatial_data/distance_cepii.csv")
## Parsed with column specification:
## cols(
## iso_o = col_character(),
## iso_d = col_character(),
## contig = col_double(),
## comlang_off = col_double(),
## comlang_ethno = col_double(),
## colony = col_double(),
## comcol = col_double(),
## curcol = col_double(),
## col45 = col_double(),
## smctry = col_double(),
## dist = col_double(),
## distcap = col_double(),
## distw = col_double(),
## distwces = col_double()
## )
# We want to merge by both ISO values, and match the ISOs to country names. I do this below.
geo_cepii_merge$iso_d <- geo_cepii_merge$iso3
merge_1 <- merge(geo_cepii_merge, distance_cepii, by = c("iso_d"), all=TRUE)
geo_cepii_merge$iso_o <- geo_cepii_merge$iso_d
merge_2 <- merge(geo_cepii_merge, merge_1, by = c("iso_o"), all=TRUE)
merge_2$COUNTRY <- merge_2$COUNTRY.x
merge_2$COUNTRY_Dyad <- merge_2$COUNTRY.y
colnames(merge_2)
## [1] "iso_o" "iso3.x" "COUNTRY.x" "iso_d.x"
## [5] "iso_d.y" "iso3.y" "COUNTRY.y" "contig"
## [9] "comlang_off" "comlang_ethno" "colony" "comcol"
## [13] "curcol" "col45" "smctry" "dist"
## [17] "distcap" "distw" "distwces" "COUNTRY"
## [21] "COUNTRY_Dyad"
Cepii_Data_Final <- merge(geo_cepii_final, merge_2, by = c("COUNTRY"), all=TRUE)
# I keep only the relevant variables.
myvarscepiifinal <- c("COUNTRY", "COUNTRY_Dyad", "dist", "distcap", "distw", "distwces", "area", "dis_int", "landlocked", "continent", "City", "City_Latitude", "City_Longitude", "Capital", "maincity", "langoff_1", "langoff_2", "langoff_3", "lang20_1", "lang20_2", "lang20_3", "lang20_4", "lang9_1", "lang9_2", "lang9_3", "lang9_4", "colonizer1", "colonizer2", "colonizer3", "colonizer4", "short_colonizer1", "short_colonizer2", "short_colonizer3", "contig", "comlang_off", "comlang_ethno", "colony", "comcol", "curcol", "col45", "smctry")
Cepii_Data_Final <- Cepii_Data_Final[myvarscepiifinal]
label(Cepii_Data_Final$landlocked) <- "Dummy Variable for Landlocked (0 = No; 1 = Yes)"
label(Cepii_Data_Final$Capital) <- "Dummy Variable for City being Capital City (0 = No; 1 = Yes)"
label(Cepii_Data_Final$maincity) <- "Dummy Variable for City being Main City (0 = No; 1 = Yes)"
# Next, we want to merge our spatial data from CEPII to our COVID dataset. I don't do this here due to the size of the resulting file, and the runtime. If you wish to view the merged file, see "Spatial_Merged.csv" in the spatial_data folder.
# I start by making a marker map which captures the number of confirmed cases on the first date of recorded data. To isolate these values, we must filter the dataframe to include observations for this date.
X1.23.20 <- Temporary_Data_Country %>%
dplyr:: filter(Date == "2020-01-23")
# First, I want to visualize the "relative intensity" of cases across the world (using a consistent scale). To do this, I cut my variable into six quantiles, based on the values of confirmed cases from the LAST available date (3-31-20).
X1.23.20$magrangeconfirmed = cut(X1.23.20$Total_Cases_Country,
breaks = c(0, 1000, 10000, 100000, 250000, 500000, 1025000),
right=FALSE, labels = c("Very Low [0-1000)", "Low
[1000-10000)", "Moderate [10000-100000)", "High [100000-250000)",
"Very High [250000-500000)", "Extreme [500000-1025000]"))
# I also want to jitter the latitude and longitude of my data points, in case our observations are clustered. We want to introduce a jitter which is large enough to differentiate markers, but not so large as to affect our spatial accuracy. I use a factor of 1.
X1.23.20$Lat <- jitter(X1.23.20$Latitude, factor = 1)
## Warning: Unknown or uninitialised column: `Latitude`.
X1.23.20$Long <- jitter(X1.23.20$Longitude, factor = 1)
## Warning: Unknown or uninitialised column: `Longitude`.
# To ease the user's interpretation of our data, we want to create an interactive label which provides information on each data point when the user hovers over the markers.
X1.23.20$labelconfirmed <- paste ("<Country: >", X1.23.20$COUNTRY, "<p>",
"<Confirmed Cases: >", X1.23.20$Total_Cases_Country, "<p>")
# Finally, we can construct our initial marker map. First, I set different colors for each quantile.
pal = colorFactor(palette = c("#CCCCCC", "#999999", "#99FF33", "#FFCC33", "#FF6600", "#990000"), domain=X1.23.20$magrangeconfirmed)
# Now, I pass the data through leaflet and generate the map.
Plot_1 <- leaflet(data=X1.23.20) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(data=X1.23.20, lng = ~longitude, lat = ~latitude,
# Here, I add the color pallete to shade the markers by intensity.
color = ~ pal(magrangeconfirmed),
weight = 5,
radius = 10,
label = lapply(X1.23.20$labelconfirmed, HTML),
clusterOptions = markerClusterOptions()) %>%
addLegend("bottomright", pal = pal, values = ~magrangeconfirmed,
title = "Total Confirmed Cases - COVID19 (January 23rd, 2020)",
opacity = 1)
## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored
Plot_1
# Now, I repeat the process for deaths. Note that in both of these maps, we see low values (which makes sense, given this was the start of the pandemic).
X1.23.20$magrangedeceased = cut(X1.23.20$Total_Deceased_Country,
breaks = c(0, 100, 500, 1000, 5000, 25000, 60000), right=FALSE,
labels = c("Very Low [0-100)", "Low [100-500)", "Moderate [500-1000)", "High [1000-5000)", "Very High
[5000-25000)", "Extreme [25000-60000]"))
X1.23.20$Lat <- jitter(X1.23.20$Latitude, factor = 1)
## Warning: Unknown or uninitialised column: `Latitude`.
X1.23.20$Long <- jitter(X1.23.20$Longitude, factor = 1)
## Warning: Unknown or uninitialised column: `Longitude`.
X1.23.20$labeldeceased <- paste ("<Country: >", X1.23.20$COUNTRY, "<p>",
"<Deceased: >", X1.23.20$Total_Deceased_Country, "<p>")
pal = colorFactor(palette = c("#CCCCCC", "#999999", "#99FF33", "#FFCC33", "#FF6600", "#990000"), domain=X1.23.20$magrangedeceased)
Plot_2 <- leaflet(data=X1.23.20) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(data=X1.23.20, lng = ~longitude, lat = ~latitude,
color = ~ pal(magrangedeceased),
weight = 5,
radius = 10,
label = lapply(X1.23.20$labeldeceased, HTML),
clusterOptions = markerClusterOptions()) %>%
addLegend("bottomright", pal = pal, values = ~magrangedeceased,
title = "Total Deaths - COVID19 (January 23rd, 2020)",
opacity = 1)
## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored
Plot_2
# Now, I repeat the same procedure for the last date in our dataset.
X4.28.20 <- Temporary_Data_Country %>%
dplyr:: filter(Date == "2020-04-28")
X4.28.20$magrangeconfirmed2 = cut(X4.28.20$Total_Cases_Country,
breaks = c(0, 1000, 10000, 100000, 250000, 500000, 1025000),
right=FALSE, labels = c("Very Low [0-1000)", "Low
[1000-10000)", "Moderate [10000-100000)", "High [100000-250000)",
"Very High [250000-500000)", "Extreme [500000-1025000]"))
X4.28.20$Lat <- jitter(X4.28.20$Latitude, factor = 1)
## Warning: Unknown or uninitialised column: `Latitude`.
X4.28.20$Long <- jitter(X4.28.20$Longitude, factor = 1)
## Warning: Unknown or uninitialised column: `Longitude`.
X4.28.20$labelconfirmed <- paste ("<Country: >", X4.28.20$COUNTRY, "<p>",
"<Confirmed Cases: >", X4.28.20$Total_Cases_Country, "<p>")
# Finally, we can construct our initial marker map. First, I set different colors for each quantile.
pal = colorFactor(palette = c("#CCCCCC", "#999999", "#99FF33", "#FFCC33", "#FF6600", "#990000"), domain=X4.28.20$magrangeconfirmed2)
# Now, I pass the data through leaflet and generate the map.
Plot_3 <- leaflet(data=X4.28.20) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(data=X4.28.20, lng = ~longitude, lat = ~latitude,
# Here, I add the color pallete to shade the markers by intensity.
color = ~ pal(magrangeconfirmed2),
weight = 5,
radius = 10,
label = lapply(X4.28.20$labelconfirmed, HTML),
clusterOptions = markerClusterOptions()) %>%
addLegend("bottomright", pal = pal, values = ~magrangeconfirmed2,
title = "Total Confirmed Cases - COVID19 (April 28th, 2020)",
opacity = 1)
## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored
Plot_3
X4.28.20$magrangedeceased2 = cut(X4.28.20$Total_Deceased_Country,
breaks = c(0, 100, 500, 1000, 5000, 25000, 60000), right=FALSE,
labels = c("Very Low [0-100)", "Low [100-500)", "Moderate [500-1000)", "High [1000-5000)", "Very High
[5000-25000)", "Extreme [25000-60000]"))
X4.28.20$Lat <- jitter(X4.28.20$Latitude, factor = 1)
## Warning: Unknown or uninitialised column: `Latitude`.
X4.28.20$Long <- jitter(X4.28.20$Longitude, factor = 1)
## Warning: Unknown or uninitialised column: `Longitude`.
X4.28.20$labeldeceased <- paste ("<Country: >", X4.28.20$COUNTRY, "<p>",
"<Deceased: >", X4.28.20$Total_Deceased_Country, "<p>")
pal = colorFactor(palette = c("#CCCCCC", "#999999", "#99FF33", "#FFCC33", "#FF6600", "#990000"), domain=X4.28.20$magrangedeceased2)
Plot_4 <- leaflet(data=X4.28.20) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(data=X4.28.20, lng = ~longitude, lat = ~latitude,
color = ~ pal(magrangedeceased2),
weight = 5,
radius = 10,
label = lapply(X4.28.20$labeldeceased, HTML),
clusterOptions = markerClusterOptions()) %>%
addLegend("bottomright", pal = pal, values = ~magrangedeceased2,
title = "Total Deaths - COVID19 (April 28th, 2020)",
opacity = 1)
## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored
Plot_4
# Now we create bubble maps for confirmed and deceased. Credit for some of these visualizations goes to: Yanchang Zhao, COVID-19 Data Analysis with R – Worldwide. RDataMining.com, 2020. URL: http://www.rdatamining.com/docs/Coronavirus-data-analysis-world.pdf.
Plot_5 <- leaflet(data = X4.28.20) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(X4.28.20$longitude, X4.28.20$latitude,
radius=2+log2(X4.28.20$Total_Cases_Country), stroke=F,
color='red', fillOpacity=0.3, label = lapply(X4.28.20$labelconfirmed, HTML))
## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored
Plot_5
Plot_6 <- leaflet(data = X4.28.20) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(X4.28.20$longitude, X4.28.20$latitude,
radius=2+log2(X4.28.20$Total_Deceased_Country), stroke=F,
color='black', fillOpacity=0.3, label = lapply(X4.28.20$labeldeceased, HTML))
## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored
Plot_6
# First, filter the data and create my labels.
# Choropleth_Country <- Temporary_Data_Country %>%
# dplyr:: filter(Date == "2020-04-28")
# Choropleth_Country$choroplethlabelsconfirmed <- paste ("<Country: >", Choropleth_Country$COUNTRY, "<p>", "<Confirmed Total: >", Choropleth_Country$Total_Cases_Country, "<p>")
# Then, I read in my shape file.
# Shape_File <- st_read("/data/UIA_World_Countries_Boundaries/UIA_World_Countries_Boundaries.shp")
# str(Shape_File)
# In order to properly use the shape file, I need to make a few edits to our country names in the data.
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Czechia", "Czech Republic", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Kyrgyzstan", "Krygz Republic", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Korea, South", "South Korea", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Russia", "Russian Federation", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Holy See", "Vatican City", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "US", "United States", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Cote d'Ivoire", "Côte d'Ivoire", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "French Guiana", "French Guiana", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Congo (Kinshasa)", "Congo DRC", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Congo (Brazzaville)", "Congo", Choropleth_Country$COUNTRY)
# Shape_File <- subset(Shape_File, is.element(Shape_File$COUNTRY, Choropleth_Country$COUNTRY))
# Choropleth_Country <- Choropleth_Country[order(match(Choropleth_Country$COUNTRY, Shape_File$COUNTRY)),]
# I also create a bin, separating the total confirmed cases into relevant quantiles. I then set colors for the bin values.
# binsconfirmed <- c(0, 5000, 25000, 50000, 100000, 250000, 1250000)
# palconfirmed <- colorBin("OrRd", domain = Choropleth_Country$Total_Cases_Country, bins = binsconfirmed)
# Plot_7 <- leaflet(data=Choropleth_Country) %>%
# addProviderTiles(providers$CartoDB.Positron) %>%
# addPolygons(data=Shape_File,
# smoothFactor = 1,
# weight = 2,
# opacity = 1,
# color = "black",
# dashArray = "",
# fillOpacity = 0.7,
# fillColor = palconfirmed(Choropleth_Country$Total_Cases_Country),
# highlight = highlightOptions(
# weight = 5,
# color = "#666",
# dashArray = "",
# fillOpacity = 0.7,
# bringToFront = TRUE),
# label = lapply(Choropleth_Country$choroplethlabelsconfirmed, HTML)) %>%
# addLegend("bottomleft", pal = palconfirmed, values = ~binsconfirmed,
# title = "Total Confirmed Cases - COVID19 (April 28th, 2020)",
# opacity = 1)
# Plot_7
# Please note: this choropleth only provides a static version of current cases (just like the Bloomberg graph). I am working on creating a web app through R Shiny which will allow the user to select a range of dates to update the choropleth.
# I now repeat this process for deaths.
# Choropleth_Country$choroplethlabelsdeceased <- paste ("<Country: >", Choropleth_Country$COUNTRY, "<p>", "<Deceased Total: >", Choropleth_Country$Total_Deceased_Country, "<p>")
# binsdeceased <- c(0, 500, 1000, 5000, 25000, 60000)
# paldeceased <- colorBin("OrRd", domain = Choropleth_Country$Total_Deceased_Country, bins = binsdeceased)
# Plot_8 <- leaflet(data=Choropleth_Country) %>%
# addProviderTiles(providers$CartoDB.Positron) %>%
# addPolygons(data=Shape_File,
# smoothFactor = 1,
# weight = 2,
# opacity = 1,
# color = "black",
# dashArray = "",
# fillOpacity = 0.7,
# fillColor = paldeceased(Choropleth_Country$Total_Deceased_Country),
# highlight = highlightOptions(
# weight = 5,
# color = "#666",
# dashArray = "",
# fillOpacity = 0.7,
# bringToFront = TRUE),
# label = lapply(Choropleth_Country$choroplethlabelsdeceased, HTML)) %>%
# addLegend("bottomleft", pal = paldeceased, values = ~binsdeceased,
# title = "Total Deaths - COVID19 (April 28th, 2020)",
# opacity = 1)
# Plot_8
# Here, I recreate a version of the first Bloomberg graph from below: https://www.bloomberg.com/graphics/2020-coronavirus-cases-world-map/. To isolate a nation of interest, simply click on it in the legend at right. You can isolate multiple nations at once. I use the final data here, which is only our developed/emerging markets of interest.
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]
Days_Since_100_Confirmed <- Final_Data_Country %>%
dplyr::filter(Total_Cases_Country >= 100)
Days_Since_100_Confirmed <- Days_Since_100_Confirmed %>%
group_by(COUNTRY) %>%
mutate(Days = row_number(COUNTRY))
Plot_9 <- plot_ly(Days_Since_100_Confirmed, x=~Days, y=~Total_Cases_Country) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="Days Since 100 Cases - COVID19", yaxis = list(type = "log"))
Plot_9
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
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# Now, I repeat the process for deaths.
Days_Since_100_Deceased <- Final_Data_Country %>%
dplyr:: filter(Total_Deceased_Country >= 100)
Days_Since_100_Deceased <- Days_Since_100_Deceased %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(Days = row_number(COUNTRY))
Plot_10 <- plot_ly(Days_Since_100_Deceased, x=~Days, y=~Total_Deceased_Country) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="Days Since 100 Deaths - COVID19")
Plot_10
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Days_Since_100_Deceased_Filtered <- Days_Since_100_Deceased %>%
dplyr::filter(COUNTRY != "Malaysia")
# I also make a facet graph for days since 100 deaths, for countries in our sample.
Plot_10A <- ggplot(data = Days_Since_100_Deceased, aes(Days, scale(Total_Deceased_Country))) + geom_line() + facet_wrap(~COUNTRY, scales = "free", ncol = 9) + theme_bw() + theme(axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.title.x=element_blank(),
axis.ticks.x=element_blank(),
strip.background = element_rect(color="white", fill="white"))
Plot_10A
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Plot_10B <- ggplot(data = Days_Since_100_Deceased_Filtered, aes(x = Days, y = scale(Total_Deceased_Country), group = COUNTRY)) + geom_line() + theme_bw() + theme(legend.title = element_blank(), axis.text.y=element_blank(), axis.title.y=element_text(size=9), axis.title.x=element_text(size=9)) + ylab("Deaths") + xlab("Days")
Plot_10B
Days_Since_100A <- Days_Since_100_Deceased_Filtered %>%
dplyr::filter(COUNTRY != "China") %>%
dplyr::filter(COUNTRY != "US") %>%
dplyr::filter(COUNTRY != "Italy")
Days_Since_100B <- Days_Since_100_Deceased_Filtered %>%
dplyr::filter(COUNTRY == "China" | COUNTRY == "US" | COUNTRY == "Italy")
Plot_10C <- ggplot(data = Days_Since_100_Deceased_Filtered, aes(x = Days, y = scale(Total_Deceased_Country), color = COUNTRY)) + geom_line() + theme_bw() + theme( axis.title.y=element_text(size=9), axis.title.x=element_text(size=9)) + gghighlight(COUNTRY == "US" | COUNTRY == "Italy" | COUNTRY == "China", use_direct_label = FALSE) + ylab("Deaths") + xlab("Days")
## Warning: Tried to calculate with group_by(), but the calculation failed.
## Falling back to ungrouped filter operation...
Plot_10C
# Here, I recreate a version of the third Bloomberg graph, plotting new global cases per day: https://www.bloomberg.com/graphics/2020-coronavirus-cases-world-map/.
New_Global_Cases <- Temporary_Data_Country %>%
group_by(Date) %>%
summarise(New_Cases = sum(New_Confirmed_Country))
New_Confirmed <- ggplot(data = New_Global_Cases, aes(x = Date, y = New_Cases)) +
geom_bar(stat = "identity", fill = "dodgerblue") +
labs(title = "New Confirmed Cases - COVID19",
subtitle = "January 23rd, 2020 - April 28th, 2020",
x = "Date", y = "New Confirmed Cases")
Plot_11 <- ggplotly(New_Confirmed)
## Warning: Removed 1 rows containing missing values (position_stack).
Plot_11
# Now, I do the same thing for deaths.
New_Global_Deaths <- Temporary_Data_Country %>%
group_by(Date) %>%
summarise(New_Deaths = sum(New_Total_Deceased_Country))
New_Deaths <- ggplot(data = New_Global_Deaths, aes(x = Date, y = New_Deaths)) +
geom_bar(stat = "identity", fill = "sandybrown") +
labs(title = "New Deaths - COVID19",
subtitle = "January 23rd, 2020 - April 28th, 2020",
x = "Date", y = "New Deaths")
Plot_12 <- ggplotly(New_Deaths)
## Warning: Removed 1 rows containing missing values (position_stack).
Plot_12
# Next, I construct a graph illustrating the seven-day rolling average of new deaths, new cases, and the mortality rate per capita. This is a version of the fourth Bloomberg plot from the above link. You will need to slide to the right (to March 2020) to see variation in these plots (or simply check the output folder). I use the Temporary Data, rather than the filtered Final Data, so that users may explore the entire set of nations available.
Temporary_Data_Country <- Temporary_Data_Country[order(Temporary_Data_Country$COUNTRY, Temporary_Data_Country$Date),]
Plot_13 <- plot_ly(Temporary_Data_Country, x=~Date, y=~rolling_average_confirmed) %>% add_lines(linetype = ~COUNTRY) %>% layout(title="Rolling Average of Confirmed Cases")
Plot_13
## Warning: plotly.js only supports 6 different linetypes
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## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
Plot_14 <- plot_ly(Temporary_Data_Country, x=~Date, y=~rolling_average_deceased) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="Rolling Average of Deaths")
Plot_14
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
Plot_15 <- plot_ly(Temporary_Data_Country, x=~Date, y=~total_rolling_average_mortality) %>% add_lines(linetype = ~COUNTRY) %>% layout(title="Total Rolling Average of Mortality Rate per Capita")
Plot_15
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
Plot_16 <- plot_ly(Temporary_Data_Country, x=~Date, y=~new_rolling_average_mortality) %>% add_lines(linetype = ~COUNTRY) %>% layout(title="New Rolling Average of Mortality Rate per Capita")
Plot_16
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
# Here, I plot the global confirmed cases over our time period.
exponential <- function(x) exp(x)
exponential_function <- ggplot(data = data.frame(x=c(0,10)), mapping=aes(x=x)) + stat_function(fun = exponential)
exponential_function
Confirmed <- ggplot(data = World_Data, aes(x = Date, y = Global_Confirmed_Cases)) +
geom_bar(stat = "identity", fill = "red") +
labs(title = "Global Confirmed Cases - COVID19",
subtitle = "January 22nd, 2020 - April 28th, 2020",
x = "Date", y = "Confirmed Cases")
Plot_17 <- ggplotly(Confirmed)
Plot_17
# Now, I plot the global deaths over our time period.
Deaths <- ggplot(data = World_Data, aes(x = Date, y = Global_Deceased)) +
geom_bar(stat = "identity", fill = "darkred") +
labs(title = "Global Deaths - COVID19",
subtitle = "January 22nd, 2020 - April 16th, 2020",
x = "Date", y = "Deaths")
Plot_18 <- ggplotly(Deaths)
Plot_18
# Now, I plot the mortality rate per capita over our time period, for our total and new mortality rates.
Total_Global_Mortality <- Temporary_Data_Country %>%
group_by(Date) %>%
summarise(Total_Mortality = sum(Total_Mortality_Rate_Per_Capita))
Total_Mortality <- ggplot(data = Total_Global_Mortality, aes(x = Date, y = Total_Mortality)) +
geom_bar(stat = "identity", fill = "darkblue") +
labs(title = "Total Global Mortality Rate - COVID19",
subtitle = "January 22nd, 2020 - April 28th, 2020",
x = "Date", y = "Total Mortality Rate Per Capita")
Plot_19 <- ggplotly(Total_Mortality)
## Warning: Removed 1 rows containing missing values (position_stack).
Plot_19
New_Global_Mortality <- Temporary_Data_Country %>%
group_by(Date) %>%
summarise(New_Mortality = sum(New_Mortality_Rate_Per_Capita))
options(scipen = 999)
New_Mortality <- ggplot(data = New_Global_Mortality, aes(x = Date, y = (New_Mortality * 100))) +
geom_bar(stat = "identity", fill = "darkred") +
labs(subtitle = "January 22nd, 2020 - April 28th, 2020", y = "New Mortality Rate Per Capita (%)") + theme_bw() + theme(axis.title.x=element_blank())
New_Mortality
## Warning: Removed 1 rows containing missing values (position_stack).
Plot_20 <- ggplotly(New_Mortality)
## Warning: Removed 1 rows containing missing values (position_stack).
Plot_20
# Now, I plot the change in the global death rate across the time period.
Death_Rate <- ggplot(World_Data, aes(x=Date)) +
geom_line(aes(y=Death_Rate, colour='Daily')) +
xlab('') + ylab('Death Rate (%)') + labs(title='Change in Death Rate (%)') +
theme(legend.position='bottom', legend.title=element_blank(),
legend.text=element_text(size=8),
legend.key.size=unit(0.5, 'cm'),
axis.text.x=element_text(angle=45, hjust=1))
Plot_21 <- ggplotly(Death_Rate)
Plot_21
# This plot compares the number of deaths to the number of confirmed cases across the time period.
Plots <- Temporary_Data_Country %>%
group_by(Date) %>%
summarise(Confirmed_Total = sum(Total_Cases_Country), Deceased_Total = sum(Total_Deceased_Country))
plot1 <- ggplot(data = Plots, aes(x = Date, y = Confirmed_Total)) +
geom_line(color = "royalblue1")
plot2 <- ggplot(data = Plots, aes(x = Date, y = Deceased_Total)) +
geom_line(color = "orangered2")
overlay2 <- ggplot(data = Plots, aes(x = Date)) +
geom_line(aes(y = Confirmed_Total), color = "royalblue1") + geom_line(aes(y = Deceased_Total), color = "orangered2") + scale_y_continuous(name = "Total Confirmed Cases", sec.axis = sec_axis(trans = ~.*1, name = "Total Deaths"))
Plot_22 <- ggplotly(overlay2)
Plot_22
# Now, I extend this analysis to generate the number of confirmed cases and deaths by country.
Plot_23 <- plot_ly(Temporary_Data_Country, x=~Date, y=~Total_Cases_Country) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="Confirmed Cases Across Countries")
Plot_23
## Warning: plotly.js only supports 6 different linetypes
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## 'NA'
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## 'NA'
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## 'NA'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
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## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
Plot_24 <- plot_ly(Temporary_Data_Country, x=~Date, y=~Total_Deceased_Country) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="Deaths Across Countries")
Plot_24
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
Plot_25 <- plot_ly(Temporary_Data_Country, x=~Date, y=~Total_Mortality_Rate_Per_Capita) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="Total Mortality Rate Across Countries")
Plot_25
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
Plot_26 <- plot_ly(Temporary_Data_Country, x=~Date, y=~New_Mortality_Rate_Per_Capita) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="New Mortality Rate Across Countries")
Plot_26
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
# Here, I filter by first deaths per country.
First_Death <- Final_Data_Country %>%
dplyr::group_by(COUNTRY) %>%
dplyr::filter(Total_Deceased_Country > 0)
# Now, I calculate the week-by-week growth rate in the rolling average of the mortality rate. This is equivalent to the log[total_total_rolling_average_mortality_(t) – total_total_rolling_average_mortality_(t-7)]. First, I create a variable for weeks by country. This tells us in what week of the year each nation had their first death.
First_Death$week_num = lubridate::week(ymd(First_Death$Date))
# Next, I generate week-over-week mortality growth rates. Should be (current week - previous week)/previous week. I use two distinct formulas for this calculation.
First_Death <- First_Death %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(total_mortality_growth = log((total_rolling_average_mortality/Lag(total_rolling_average_mortality,7))))
First_Death <- First_Death %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(total_mortality_growth_b <- (total_rolling_average_mortality-Lag(total_rolling_average_mortality,7))/Lag(total_rolling_average_mortality,7))
First_Death$total_mortality_growth_b <- log(First_Death$`... <- NULL`,10)
First_Death <- First_Death %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(new_mortality_growth <- log((new_rolling_average_mortality/Lag(new_rolling_average_mortality,7))))
First_Death$new_mortality_growth <- First_Death$`... <- NULL`
First_Death <- First_Death %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(new_mortality_growth_b <- (new_rolling_average_mortality-Lag(new_rolling_average_mortality,7))/Lag(new_rolling_average_mortality,7))
First_Death$new_mortality_growth_b <- log(First_Death$`... <- NULL`,10)
## Warning: NaNs produced
# Please see the First Death dataset in the data folder.
# The following code allows us to visualize the rolling average of the mortality rate by country.
Plot_27A <- plot_ly(First_Death, x=~Date, y=~total_rolling_average_mortality) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="Total Rolling Average of Mortality Rate Over Time - COVID19")
Plot_27A
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
Plot_27B <- plot_ly(First_Death, x=~Date, y=~new_rolling_average_mortality) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="New Rolling Average of Mortality Rate Over Time - COVID19")
Plot_27B
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
# Here, I implement a log axis.
Plot_28 <- Plot_27A %>% layout(yaxis = list(type = "log"))
Plot_28
## Warning: plotly.js only supports 6 different linetypes
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
First_Death_Filtered <- First_Death %>%
dplyr::filter(COUNTRY != "Singapore") %>%
dplyr::filter(COUNTRY != "Vietnam")
Plot_28A <- ggplot(data = First_Death_Filtered, aes(x = Date, y = log(total_rolling_average_mortality), group = COUNTRY)) + geom_line() + theme_bw() + theme(legend.title = element_blank(), axis.text.y=element_blank(), axis.title.y=element_text(size=9), axis.title.x=element_blank()) + ylab("Rolling Average Cumulative Mortality")
Plot_28A
# Here, I add a facet wrap for each nation in our sample.
Plot_28B <- ggplot(data = First_Death_Filtered, aes(Date, log(total_rolling_average_mortality))) + geom_line(size=.7) + facet_wrap(~COUNTRY, switch="x", ncol = 7) + theme_bw() + theme(axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.x = element_blank(),
strip.background = element_rect(color="white", fill="white"))
## Warning: 'switch' is deprecated.
## Use 'strip.position' instead.
## See help("Deprecated")
Plot_28B
Plot_28C <- ggplot(data = First_Death_Filtered, aes(x = Date, y = new_rolling_average_mortality, group = COUNTRY)) + geom_line() + theme_bw() + theme(legend.title = element_blank(), axis.text.y=element_blank(), axis.title.y=element_text(size=9), axis.title.x=element_blank()) + ylab("Rolling Average New Mortality")
Plot_28C
library(gghighlight)
Plot_28D <- ggplot(data = First_Death_Filtered, aes(x = Date, y = new_rolling_average_mortality, color = COUNTRY)) + geom_line() + theme_bw() + theme(legend.title = element_blank(), legend.position = "none", axis.title.y=element_text(size=9), axis.title.x=element_text(size=9)) + ylab("Rolling Average New Mortality") + gghighlight(COUNTRY == "Italy" | COUNTRY == "China" | COUNTRY == "US")
## Warning: Tried to calculate with group_by(), but the calculation failed.
## Falling back to ungrouped filter operation...
## label_key: COUNTRY
Plot_28D
Plot_28E <- ggplot(data = First_Death_Filtered, aes(x = Date, y = log(total_rolling_average_mortality), color = COUNTRY)) + geom_line() + theme_bw() + theme(legend.title = element_blank(), legend.position = "none", axis.title.y=element_text(size=9), axis.title.x=element_text(size=9)) + ylab("Rolling Average Cumulative Mortality (log)") + gghighlight(COUNTRY == "Italy" | COUNTRY == "China" | COUNTRY == "US")
## Warning: Tried to calculate with group_by(), but the calculation failed.
## Falling back to ungrouped filter operation...
## label_key: COUNTRY
Plot_28E
countriez <- unique(Final_Data_Country$COUNTRY)[-c(8,47,59)]
country_plots<-list()
for(i in 1:length(countriez)) {
country_plots[[i]] <- ggplot(data=subset(Final_Data_Country,COUNTRY==countriez[i]), aes_string(x="Date",y="new_rolling_average_mortality"))+geom_line(size=1)+theme_bw()+ylab("")+xlab(countriez[i])+theme(axis.text=element_blank())
}
Plot28F <- do.call("grid.arrange", c(country_plots))
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
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## Warning: Removed 23 row(s) containing missing values (geom_path).
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## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
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## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
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## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
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## Warning: Removed 23 row(s) containing missing values (geom_path).
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## Warning: Removed 23 row(s) containing missing values (geom_path).
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## Warning: Removed 23 row(s) containing missing values (geom_path).
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## Warning: Removed 10 row(s) containing missing values (geom_path).
## Warning: Removed 10 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
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## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
## Warning: Removed 23 row(s) containing missing values (geom_path).
# Next, I create initial visualizations of the mortality growth rates by country.
Plot_29 <- plot_ly(First_Death, x=~Date, y=~total_mortality_growth) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="Weekly Growth Rate in Total Rolling Average Mortality per Capita - COVID19")
Plot_29
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
Plot_30 <- plot_ly(First_Death, x=~Date, y=~total_mortality_growth_b) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="Alternate Weekly Growth Rate in Total Rolling Average Mortality per Capita - COVID19")
Plot_30
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
Plot_31 <- plot_ly(First_Death, x=~Date, y=~new_mortality_growth) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(title="Weekly Growth Rate in New Rolling Average Mortality per Capita - COVID19")
Plot_31
## Warning: plotly.js only supports 6 different linetypes
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'
# First, we generate extensive summary statistics for each variable with describe in the Hmisc function.
Hmisc::describe(Final_Data_Country)
## Final_Data_Country
##
## 160 Variables 7029 Observations
## --------------------------------------------------------------------------------
## COUNTRY
## n missing distinct
## 7029 0 59
##
## lowest : Argentina Australia Austria Belgium Brazil
## highest: Turkey United Arab Emirates United Kingdom US Vietnam
## --------------------------------------------------------------------------------
## X1
## n missing distinct Info Mean Gmd .05 .10
## 7029 0 7029 1 3515 2343 352.4 703.8
## .25 .50 .75 .90 .95
## 1758.0 3515.0 5272.0 6326.2 6677.6
##
## lowest : 1 2 3 4 5, highest: 7025 7026 7027 7028 7029
## --------------------------------------------------------------------------------
## Date
## n missing distinct Info Mean Gmd .05
## 7029 0 120 1 2020-02-29 39.81 2020-01-07
## .10 .25 .50 .75 .90 .95
## 2020-01-13 2020-01-31 2020-03-01 2020-03-31 2020-04-17 2020-04-23
##
## lowest : 2020-01-01 2020-01-02 2020-01-03 2020-01-04 2020-01-05
## highest: 2020-04-25 2020-04-26 2020-04-27 2020-04-28 2020-04-29
## --------------------------------------------------------------------------------
## Total_Cases_Country
## n missing distinct Info Mean Gmd .05 .10
## 5710 1319 2459 0.982 10837 19849 0 0
## .25 .50 .75 .90 .95
## 0 98 2620 15059 57903
##
## lowest : 0 1 2 3 4
## highest: 905358 938154 965785 988197 1012582
## --------------------------------------------------------------------------------
## Total_Deceased_Country
## n missing distinct Info Mean Gmd .05 .10
## 5710 1319 911 0.857 690.8 1312 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 0.0 50.0 533.2 2835.2
##
## lowest : 0 1 2 3 4, highest: 51493 53755 54881 56259 58355
## --------------------------------------------------------------------------------
## New_Confirmed_Country
## n missing distinct Info Mean Gmd .05 .10
## 5710 1319 1090 0.938 501.3 914.2 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 8.0 133.8 791.1 2330.6
##
## lowest : 0 1 2 3 4, highest: 32796 33283 33755 34126 36188
## --------------------------------------------------------------------------------
## New_Total_Deceased_Country
## n missing distinct Info Mean Gmd .05 .10
## 5710 1319 375 0.752 36.05 67.98 0 0
## .25 .50 .75 .90 .95
## 0 0 4 35 144
##
## lowest : 0 1 2 3 4, highest: 2342 2392 2427 2472 2584
## --------------------------------------------------------------------------------
## Population
## n missing distinct Info Mean Gmd .05 .10
## 5710 1319 59 1 92628187 144353301 625976 1886202
## .25 .50 .75 .90 .95
## 5540718 19116209 67886004 145934460 331002647
##
## lowest : 341250 441539 625976 1207361 1326539
## highest: 212559409 273523621 331002647 1380004385 1439323774
## --------------------------------------------------------------------------------
## Total_Mortality_Rate_Per_Capita
## n missing distinct Info Mean Gmd
## 5710 1319 2120 0.857 0.00001449 0.00002677
## .05 .10 .25 .50 .75 .90
## 0.000000000 0.000000000 0.000000000 0.000000000 0.000002311 0.000022685
## .95
## 0.000073830
##
## lowest : 0.0000000000000000 0.0000000007246354 0.0000000014492708 0.0000000021739061 0.0000000028985415
## highest: 0.0005762917425392 0.0005968273668429 0.0006120996588670 0.0006218497662045 0.0006325489990350
## --------------------------------------------------------------------------------
## New_Mortality_Rate_Per_Capita
## n missing distinct Info Mean Gmd
## 5710 1319 1155 0.752 0.0000007797 0.000001421
## .05 .10 .25 .50 .75 .90
## 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000001185 0.0000016293
## .95
## 0.0000050630
##
## lowest : 0.0000000000000000 0.0000000006947707 0.0000000007246354 0.0000000013895414 0.0000000014492708
## highest: 0.0000282149123836 0.0000347725066991 0.0000359804845993 0.0000427969313220 0.0000445542910238
## --------------------------------------------------------------------------------
## Total_Cases_Country_Per_Capita
## n missing distinct Info Mean Gmd
## 5710 1319 3450 0.982 0.0003067 0.0005238
## .05 .10 .25 .50 .75 .90
## 0.000000000 0.000000000 0.000000000 0.000003982 0.000171354 0.000999345
## .95
## 0.001822912
##
## lowest : 0.0000000000000000 0.0000000007246354 0.0000000014492708 0.0000000021739061 0.0000000030211239
## highest: 0.0059027822152926 0.0059283423006633 0.0059475123646913 0.0059570973967053 0.0059762674607333
## --------------------------------------------------------------------------------
## rolling_average_confirmed
## n missing distinct Info Mean Gmd .05 .10
## 5710 1319 1971 0.973 464.3 847.4 0.000 0.000
## .25 .50 .75 .90 .95
## 0.000 7.571 120.143 719.114 2248.364
##
## lowest : 0.0000000 0.1428571 0.2000000 0.2500000 0.2857143
## highest: 30773.1428571 31106.5714286 31215.8571429 31308.2857143 31595.4285714
## --------------------------------------------------------------------------------
## rolling_average_deceased
## n missing distinct Info Mean Gmd .05 .10
## 5710 1319 702 0.842 33.42 63.09 0.000 0.000
## .25 .50 .75 .90 .95
## 0.000 0.000 3.143 30.043 130.550
##
## lowest : 0.0000000 0.1428571 0.2857143 0.4285714 0.5714286
## highest: 2117.7142857 2122.7142857 2128.1428571 2154.0000000 2201.5714286
## --------------------------------------------------------------------------------
## total_rolling_average_mortality
## n missing distinct Info Mean Gmd
## 5710 1319 2611 0.858 0.00001224 0.00002276
## .05 .10 .25 .50 .75 .90
## 0.000000000 0.000000000 0.000000000 0.000000000 0.000001812 0.000017169
## .95
## 0.000060324
##
## lowest : 0.0000000000000000 0.0000000001035193 0.0000000002070387 0.0000000004140774 0.0000000004315891
## highest: 0.0005225490522846 0.0005405947629573 0.0005579871794594 0.0005749851541981 0.0005914161189021
## --------------------------------------------------------------------------------
## new_rolling_average_mortality
## n missing distinct Info Mean Gmd
## 5710 1319 1824 0.842 0.0000007276 0.000001322
## .05 .10 .25 .50 .75 .90
## 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000001293 0.0000014430
## .95
## 0.0000047985
##
## lowest : 0.00000000000000000 0.00000000009925296 0.00000000010351934 0.00000000020703868 0.00000000031055802
## highest: 0.00002642759814352 0.00002653853488946 0.00002711787122936 0.00002799303889176 0.00002876959611333
## --------------------------------------------------------------------------------
## latitude
## n missing distinct Info Mean Gmd .05 .10
## 5710 1319 59 1 30.98 28.87 -35.68 -14.24
## .25 .50 .75 .90 .95
## 20.59 37.09 51.17 58.60 61.52
##
## lowest : -40.90056 -38.41610 -35.67515 -30.55948 -25.27440
## highest: 60.12816 60.47202 61.52401 61.92411 64.96305
## --------------------------------------------------------------------------------
## longitude
## n missing distinct Info Mean Gmd .05 .10
## 5710 1319 59 1 26.04 66.37 -95.713 -71.543
## .25 .50 .75 .90 .95
## 2.214 19.503 51.184 113.921 133.775
##
## lowest : -106.34677 -102.55278 -95.71289 -75.01515 -74.29733
## highest: 121.77402 127.76692 133.77514 138.25292 174.88597
## --------------------------------------------------------------------------------
## C1_School.closing
## n missing distinct Info Mean Gmd
## 6593 436 4 0.744 1.174 1.428
##
## Value 0 1 2 3
## Frequency 3887 117 144 2445
## Proportion 0.590 0.018 0.022 0.371
## --------------------------------------------------------------------------------
## C1_Flag
## n missing distinct Info Sum Mean Gmd
## 2699 4330 2 0.356 2328 0.8625 0.2372
##
## --------------------------------------------------------------------------------
## C2_Workplace.closing
## n missing distinct Info Mean Gmd
## 6505 524 4 0.742 0.8673 1.206
##
## Value 0 1 2 3
## Frequency 4086 546 523 1350
## Proportion 0.628 0.084 0.080 0.208
## --------------------------------------------------------------------------------
## C2_Flag
## n missing distinct Info Sum Mean Gmd
## 2417 4612 2 0.451 1972 0.8159 0.3006
##
## --------------------------------------------------------------------------------
## C3_Cancel.public.events
## n missing distinct Info Mean Gmd
## 6550 479 3 0.757 0.8206 0.9662
##
## Value 0 1 2
## Frequency 3723 279 2548
## Proportion 0.568 0.043 0.389
## --------------------------------------------------------------------------------
## C3_Flag
## n missing distinct Info Sum Mean Gmd
## 2816 4213 2 0.467 2273 0.8072 0.3114
##
## --------------------------------------------------------------------------------
## C4_Restrictions.on.gatherings
## n missing distinct Info Mean Gmd
## 2522 4507 5 0.888 2.065 1.933
##
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##
## Value 0 1 2 3 4
## Frequency 944 140 224 236 978
## Proportion 0.374 0.056 0.089 0.094 0.388
## --------------------------------------------------------------------------------
## C4_Flag
## n missing distinct Info Sum Mean Gmd
## 1578 5451 2 0.238 1441 0.9132 0.1587
##
## --------------------------------------------------------------------------------
## C5_Close.public.transport
## n missing distinct Info Mean Gmd
## 6547 482 3 0.576 0.3689 0.5838
##
## Value 0 1 2
## Frequency 4901 877 769
## Proportion 0.749 0.134 0.117
## --------------------------------------------------------------------------------
## C5_Flag
## n missing distinct Info Sum Mean Gmd
## 1710 5319 2 0.559 1287 0.7526 0.3726
##
## --------------------------------------------------------------------------------
## C6_Stay.at.home.requirements
## n missing distinct Info Mean Gmd
## 2425 4604 4 0.86 1.066 1.077
##
## Value 0 1 2 3
## Frequency 1033 321 950 121
## Proportion 0.426 0.132 0.392 0.050
## --------------------------------------------------------------------------------
## C6_Flag
## n missing distinct Info Sum Mean Gmd
## 1392 5637 2 0.497 1100 0.7902 0.3318
##
## --------------------------------------------------------------------------------
## C7_Restrictions.on.internal.movement
## n missing distinct Info Mean Gmd
## 6523 506 3 0.709 0.6376 0.8634
##
## Value 0 1 2
## Frequency 4205 477 1841
## Proportion 0.645 0.073 0.282
## --------------------------------------------------------------------------------
## C7_Flag
## n missing distinct Info Sum Mean Gmd
## 2302 4727 2 0.609 1650 0.7168 0.4062
##
## --------------------------------------------------------------------------------
## C8_International.travel.controls
## n missing distinct Info Mean Gmd
## 6573 456 5 0.874 1.55 1.728
##
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##
## Value 0 1 2 3 4
## Frequency 3094 415 375 1733 956
## Proportion 0.471 0.063 0.057 0.264 0.145
## --------------------------------------------------------------------------------
## E1_Income.support
## n missing distinct Info Mean Gmd
## 238 6791 2 0.321 0.2437 0.4298
##
## Value 0 2
## Frequency 209 29
## Proportion 0.878 0.122
## --------------------------------------------------------------------------------
## E1_Flag
## n missing distinct
## 29 7000 2
##
## Value FALSE TRUE
## Frequency 27 2
## Proportion 0.931 0.069
## --------------------------------------------------------------------------------
## E2_Debt.contract.relief
## n missing distinct Info Mean Gmd
## 240 6789 3 0.682 0.5792 0.82
##
## Value 0 1 2
## Frequency 161 19 60
## Proportion 0.671 0.079 0.250
## --------------------------------------------------------------------------------
## E3_Fiscal.measures
## n missing distinct Info Mean Gmd .05
## 6006 1023 154 0.103 1240873079 2473644332 0
## .10 .25 .50 .75 .90 .95
## 0 0 0 0 0 0
##
## lowest : 0.00 10.75 310.70 434.00 536507.00
## highest: 420453000000.00 452656000000.00 476000000000.00 990863000000.00 1957600000000.00
##
## 0 (5952, 0.991), 20000000000 (21, 0.003), 40000000000 (15, 0.002), 60000000000
## (3, 0.000), 80000000000 (3, 0.000), 120000000000 (2, 0.000), 200000000000 (1,
## 0.000), 220000000000 (1, 0.000), 240000000000 (2, 0.000), 360000000000 (1,
## 0.000), 420000000000 (1, 0.000), 460000000000 (1, 0.000), 480000000000 (1,
## 0.000), 1000000000000 (1, 0.000), 1960000000000 (1, 0.000)
##
## For the frequency table, variable is rounded to the nearest 2e+10
## --------------------------------------------------------------------------------
## H1_Public.information.campaigns
## n missing distinct Info Mean Gmd
## 6543 486 3 0.761 1.177 0.9667
##
## Value 0 1 2
## Frequency 2542 302 3699
## Proportion 0.389 0.046 0.565
## --------------------------------------------------------------------------------
## H1_Flag
## n missing distinct Info Sum Mean Gmd
## 3992 3037 2 0.163 3762 0.9424 0.1086
##
## --------------------------------------------------------------------------------
## H2_Testing.policy
## n missing distinct Info Mean Gmd
## 6026 1003 4 0.871 0.8432 0.9013
##
## Value 0 1 2 3
## Frequency 2475 2298 976 277
## Proportion 0.411 0.381 0.162 0.046
## --------------------------------------------------------------------------------
## H3_Contact.tracing
## n missing distinct Info Mean Gmd
## 5855 1174 3 0.847 0.7643 0.8828
##
## Value 0 1 2
## Frequency 2890 1455 1510
## Proportion 0.494 0.249 0.258
## --------------------------------------------------------------------------------
## H4_Emergency.investment.in.healthcare
## n missing distinct Info Mean Gmd .05 .10
## 5799 1230 89 0.113 82850864 165312400 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 0
##
## lowest : 0.00 0.03 35.00 562.00 585.00
## highest: 6700000000.00 14190000000.00 57458000000.00 62997723800.00 242400000000.00
##
## 0 (5738, 0.989), 500000000 (11, 0.002), 1000000000 (3, 0.001), 1500000000 (35,
## 0.006), 2000000000 (3, 0.001), 3000000000 (1, 0.000), 4000000000 (1, 0.000),
## 4500000000 (1, 0.000), 6500000000 (2, 0.000), 14000000000 (1, 0.000),
## 57500000000 (1, 0.000), 63000000000 (1, 0.000), 242500000000 (1, 0.000)
##
## For the frequency table, variable is rounded to the nearest 5e+08
## --------------------------------------------------------------------------------
## H5_Investment.in.vaccines
## n missing distinct Info Mean Gmd .05 .10
## 5662 1367 25 0.046 434842 868686 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 0
##
## lowest : 0 1 191 246961 1067577
## highest: 190372000 200000000 254591132 286175609 826000000
##
## 0 (5638, 0.996), 2000000 (2, 0.000), 4000000 (1, 0.000), 6000000 (2, 0.000),
## 8000000 (2, 0.000), 10000000 (1, 0.000), 18000000 (1, 0.000), 26000000 (6,
## 0.001), 54000000 (1, 0.000), 100000000 (1, 0.000), 156000000 (1, 0.000),
## 174000000 (1, 0.000), 190000000 (1, 0.000), 200000000 (1, 0.000), 254000000 (1,
## 0.000), 286000000 (1, 0.000), 826000000 (1, 0.000)
##
## For the frequency table, variable is rounded to the nearest 2000000
## --------------------------------------------------------------------------------
## StringencyIndex
## n missing distinct Info Mean Gmd .05 .10
## 6494 535 323 0.965 31.39 36.8 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 13.89 64.02 83.87 90.74
##
## lowest : 0.00 2.78 3.17 3.97 5.42, highest: 94.71 94.84 96.03 97.35 100.00
## --------------------------------------------------------------------------------
## LegacyStringencyIndex
## n missing distinct Info Mean Gmd .05 .10
## 6497 532 183 0.964 34.5 39.16 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 17.14 74.52 88.81 92.38
##
## lowest : 0.00 2.86 4.76 5.71 6.43, highest: 91.43 92.38 93.57 94.29 97.14
## --------------------------------------------------------------------------------
## Oxford_Cases
## n missing distinct Info Mean Gmd .05 .10
## 5630 1399 2408 0.976 10841 19830 0 0
## .25 .50 .75 .90 .95
## 0 83 2782 15315 60342
##
## lowest : 0 1 2 3 4
## highest: 890524 939053 965910 988451 1012583
## --------------------------------------------------------------------------------
## Oxford_Deaths
## n missing distinct Info Mean Gmd .05 .10
## 5630 1399 907 0.848 687.9 1305 0.0 0.0
## .25 .50 .75 .90 .95
## 0.0 0.0 53.0 542.1 2866.2
##
## lowest : 0 1 2 3 4, highest: 51017 53189 54876 56245 58355
## --------------------------------------------------------------------------------
## Lagged_School_Closing_One_Week
## n missing distinct Info Mean Gmd
## 6322 707 4 0.724 1.106 1.396
##
## Value 0 1 2 3
## Frequency 3887 105 104 2226
## Proportion 0.615 0.017 0.016 0.352
## --------------------------------------------------------------------------------
## Lagged_School_Closing_General_One_Week
## n missing distinct Info Sum Mean Gmd
## 2428 4601 2 0.367 2081 0.8571 0.2451
##
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_One_Week
## n missing distinct Info Mean Gmd
## 6234 795 4 0.711 0.8022 1.153
##
## Value 0 1 2 3
## Frequency 4083 508 436 1207
## Proportion 0.655 0.081 0.070 0.194
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_General_One_Week
## n missing distinct Info Sum Mean Gmd
## 2149 4880 2 0.462 1740 0.8097 0.3083
##
## --------------------------------------------------------------------------------
## Lagged_Public_Events_One_Week
## n missing distinct Info Mean Gmd
## 6305 724 3 0.745 0.7764 0.9483
##
## Value 0 1 2
## Frequency 3723 269 2313
## Proportion 0.590 0.043 0.367
## --------------------------------------------------------------------------------
## Lagged_Public_Events_General_One_Week
## n missing distinct Info Sum Mean Gmd
## 2567 4462 2 0.487 2043 0.7959 0.325
##
## --------------------------------------------------------------------------------
## Lagged_Gatherings_One_Week
## n missing distinct Info Mean Gmd
## 2296 4733 5 0.882 1.951 1.94
##
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##
## Value 0 1 2 3 4
## Frequency 933 124 202 196 841
## Proportion 0.406 0.054 0.088 0.085 0.366
## --------------------------------------------------------------------------------
## Lagged_Gatherings_General_One_Week
## n missing distinct Info Sum Mean Gmd
## 1363 5666 2 0.234 1247 0.9149 0.1558
##
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_One_Week
## n missing distinct Info Mean Gmd
## 6306 723 3 0.546 0.3416 0.551
##
## Value 0 1 2
## Frequency 4836 786 684
## Proportion 0.767 0.125 0.108
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_General_One_Week
## n missing distinct Info Sum Mean Gmd
## 1534 5495 2 0.576 1136 0.7405 0.3845
##
## --------------------------------------------------------------------------------
## Lagged_Lockdown_One_Week
## n missing distinct Info Mean Gmd
## 2194 4835 4 0.847 1.013 1.072
##
## Value 0 1 2 3
## Frequency 1004 256 835 99
## Proportion 0.458 0.117 0.381 0.045
## --------------------------------------------------------------------------------
## Lagged_Lockdown_General_One_Week
## n missing distinct Info Sum Mean Gmd
## 1190 5839 2 0.518 926 0.7782 0.3456
##
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_One_Week
## n missing distinct Info Mean Gmd
## 6285 744 3 0.686 0.6005 0.8359
##
## Value 0 1 2
## Frequency 4185 426 1674
## Proportion 0.666 0.068 0.266
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_General_One_Week
## n missing distinct Info Sum Mean Gmd
## 2084 4945 2 0.63 1459 0.7001 0.4201
##
## --------------------------------------------------------------------------------
## Lagged_International_Travel_One_Week
## n missing distinct Info Mean Gmd
## 6316 713 5 0.863 1.476 1.694
##
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##
## Value 0 1 2 3 4
## Frequency 3086 415 346 1658 811
## Proportion 0.489 0.066 0.055 0.263 0.128
## --------------------------------------------------------------------------------
## Lagged_Income_Support_One_Week
## n missing distinct Info Mean Gmd
## 226 6803 2 0.264 0.1947 0.353
##
## Value 0 2
## Frequency 204 22
## Proportion 0.903 0.097
## --------------------------------------------------------------------------------
## Lagged_Income_Support_General_One_Week
## n missing distinct
## 22 7007 2
##
## Value FALSE TRUE
## Frequency 20 2
## Proportion 0.909 0.091
## --------------------------------------------------------------------------------
## Lagged_Debt_Relief_One_Week
## n missing distinct Info Mean Gmd
## 226 6803 3 0.625 0.5221 0.7722
##
## Value 0 1 2
## Frequency 161 12 53
## Proportion 0.712 0.053 0.235
## --------------------------------------------------------------------------------
## Lagged_Fiscal_Measures_One_Week
## n missing distinct Info Mean Gmd .05
## 5981 1048 153 0.103 1246051988 2483935204 0
## .10 .25 .50 .75 .90 .95
## 0 0 0 0 0 0
##
## lowest : 0.00 10.75 310.70 434.00 536507.00
## highest: 420453000000.00 452656000000.00 476000000000.00 990863000000.00 1957600000000.00
##
## 0 (5927, 0.991), 20000000000 (21, 0.004), 40000000000 (15, 0.003), 60000000000
## (3, 0.001), 80000000000 (3, 0.001), 120000000000 (2, 0.000), 200000000000 (1,
## 0.000), 220000000000 (1, 0.000), 240000000000 (2, 0.000), 360000000000 (1,
## 0.000), 420000000000 (1, 0.000), 460000000000 (1, 0.000), 480000000000 (1,
## 0.000), 1000000000000 (1, 0.000), 1960000000000 (1, 0.000)
##
## For the frequency table, variable is rounded to the nearest 2e+10
## --------------------------------------------------------------------------------
## Lagged_Campaign_One_Week
## n missing distinct Info Mean Gmd
## 6309 720 3 0.768 1.147 0.9763
##
## Value 0 1 2
## Frequency 2542 296 3471
## Proportion 0.403 0.047 0.550
## --------------------------------------------------------------------------------
## Lagged_Campaign_General_One_Week
## n missing distinct Info Sum Mean Gmd
## 3758 3271 2 0.172 3528 0.9388 0.1149
##
## --------------------------------------------------------------------------------
## Lagged_Testing_One_Week
## n missing distinct Info Mean Gmd
## 5996 1033 4 0.87 0.8362 0.8947
##
## Value 0 1 2 3
## Frequency 2475 2289 971 261
## Proportion 0.413 0.382 0.162 0.044
## --------------------------------------------------------------------------------
## Lagged_Tracing_One_Week
## n missing distinct Info Mean Gmd
## 5836 1193 3 0.846 0.7613 0.8814
##
## Value 0 1 2
## Frequency 2889 1451 1496
## Proportion 0.495 0.249 0.256
## --------------------------------------------------------------------------------
## Lagged_Health_Care_One_Week
## n missing distinct Info Mean Gmd .05 .10
## 5646 1383 25 0.047 436074 871145 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 0
##
## lowest : 0 1 191 246961 1067577
## highest: 190372000 200000000 254591132 286175609 826000000
##
## 0 (5622, 0.996), 2000000 (2, 0.000), 4000000 (1, 0.000), 6000000 (2, 0.000),
## 8000000 (2, 0.000), 10000000 (1, 0.000), 18000000 (1, 0.000), 26000000 (6,
## 0.001), 54000000 (1, 0.000), 100000000 (1, 0.000), 156000000 (1, 0.000),
## 174000000 (1, 0.000), 190000000 (1, 0.000), 200000000 (1, 0.000), 254000000 (1,
## 0.000), 286000000 (1, 0.000), 826000000 (1, 0.000)
##
## For the frequency table, variable is rounded to the nearest 2000000
## --------------------------------------------------------------------------------
## Lagged_Stringency_Index_One_Week
## n missing distinct Info Mean Gmd .05 .10
## 6296 733 320 0.962 29.84 35.73 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 11.11 61.34 82.27 90.47
##
## lowest : 0.00 2.78 3.17 3.97 5.42, highest: 94.71 94.84 96.03 97.35 100.00
## --------------------------------------------------------------------------------
## Lagged_Legacy_Stringency_Index_One_Week
## n missing distinct Info Mean Gmd .05 .10
## 6298 731 181 0.961 32.99 38.33 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 14.29 70.48 87.14 92.38
##
## lowest : 0.00 2.86 4.76 5.71 6.43, highest: 91.43 92.38 93.57 94.29 97.14
## --------------------------------------------------------------------------------
## Lagged_School_Closing_Two_Weeks
## n missing distinct Info Mean Gmd
## 5932 1097 4 0.686 0.9936 1.329
##
## Value 0 1 2 3
## Frequency 3887 91 59 1895
## Proportion 0.655 0.015 0.010 0.319
## --------------------------------------------------------------------------------
## Lagged_School_Closing_General_Two_Weeks
## n missing distinct Info Sum Mean Gmd
## 2038 4991 2 0.382 1733 0.8503 0.2546
##
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_Two_Weeks
## n missing distinct Info Mean Gmd
## 5846 1183 4 0.655 0.6924 1.047
##
## Value 0 1 2 3
## Frequency 4080 457 336 973
## Proportion 0.698 0.078 0.057 0.166
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_General_Two_Weeks
## n missing distinct Info Sum Mean Gmd
## 1764 5265 2 0.478 1413 0.801 0.319
##
## --------------------------------------------------------------------------------
## Lagged_Public_Events_Two_Weeks
## n missing distinct Info Mean Gmd
## 5925 1104 3 0.717 0.6996 0.908
##
## Value 0 1 2
## Frequency 3723 259 1943
## Proportion 0.628 0.044 0.328
## --------------------------------------------------------------------------------
## Lagged_Public_Events_General_Two_Weeks
## n missing distinct Info Sum Mean Gmd
## 2182 4847 2 0.516 1700 0.7791 0.3444
##
## --------------------------------------------------------------------------------
## Lagged_Gatherings_Two_Weeks
## n missing distinct Info Mean Gmd
## 1940 5089 5 0.861 1.71 1.907
##
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##
## Value 0 1 2 3 4
## Frequency 918 97 166 147 612
## Proportion 0.473 0.050 0.086 0.076 0.315
## --------------------------------------------------------------------------------
## Lagged_Gatherings_General_Two_Weeks
## n missing distinct Info Sum Mean Gmd
## 1022 6007 2 0.186 954 0.9335 0.1243
##
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_Two_Weeks
## n missing distinct Info Mean Gmd
## 5921 1108 3 0.491 0.2964 0.4932
##
## Value 0 1 2
## Frequency 4721 645 555
## Proportion 0.797 0.109 0.094
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_General_Two_Weeks
## n missing distinct Info Sum Mean Gmd
## 1264 5765 2 0.601 914 0.7231 0.4008
##
## --------------------------------------------------------------------------------
## Lagged_Lockdown_Two_Weeks
## n missing distinct Info Mean Gmd
## 1836 5193 4 0.813 0.8851 1.038
##
## Value 0 1 2 3
## Frequency 968 177 625 66
## Proportion 0.527 0.096 0.340 0.036
## --------------------------------------------------------------------------------
## Lagged_Lockdown_General_Two_Weeks
## n missing distinct Info Sum Mean Gmd
## 868 6161 2 0.543 662 0.7627 0.3624
##
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_Two_Weeks
## n missing distinct Info Mean Gmd
## 5901 1128 3 0.636 0.5291 0.7747
##
## Value 0 1 2
## Frequency 4161 358 1382
## Proportion 0.705 0.061 0.234
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_General_Two_Weeks
## n missing distinct Info Sum Mean Gmd
## 1724 5305 2 0.65 1177 0.6827 0.4335
##
## --------------------------------------------------------------------------------
## Lagged_International_Travel_Two_Weeks
## n missing distinct Info Mean Gmd
## 5929 1100 5 0.842 1.356 1.628
##
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##
## Value 0 1 2 3 4
## Frequency 3072 415 304 1536 602
## Proportion 0.518 0.070 0.051 0.259 0.102
## --------------------------------------------------------------------------------
## Lagged_Income_Support_Two_Weeks
## n missing distinct Info Mean Gmd
## 212 6817 2 0.197 0.1415 0.2642
##
## Value 0 2
## Frequency 197 15
## Proportion 0.929 0.071
## --------------------------------------------------------------------------------
## Lagged_Income_Support_General_Two_Weeks
## n missing distinct
## 15 7014 2
##
## Value FALSE TRUE
## Frequency 13 2
## Proportion 0.867 0.133
## --------------------------------------------------------------------------------
## Lagged_Debt_Relief_Two_Weeks
## n missing distinct Info Mean Gmd
## 212 6817 3 0.552 0.4575 0.7085
##
## Value 0 1 2
## Frequency 161 5 46
## Proportion 0.759 0.024 0.217
## --------------------------------------------------------------------------------
## Lagged_Fiscal_Measures_Two_Weeks
## n missing distinct Info Mean Gmd .05
## 5767 1262 137 0.096 1250385439 2492785792 0
## .10 .25 .50 .75 .90 .95
## 0 0 0 0 0 0
##
## lowest : 0.00 10.75 310.70 434.00 536507.00
## highest: 420453000000.00 452656000000.00 476000000000.00 990863000000.00 1957600000000.00
##
## 0 (5714, 0.991), 20000000000 (21, 0.004), 40000000000 (15, 0.003), 60000000000
## (3, 0.001), 80000000000 (3, 0.001), 120000000000 (2, 0.000), 200000000000 (1,
## 0.000), 240000000000 (2, 0.000), 360000000000 (1, 0.000), 420000000000 (1,
## 0.000), 460000000000 (1, 0.000), 480000000000 (1, 0.000), 1000000000000 (1,
## 0.000), 1960000000000 (1, 0.000)
##
## For the frequency table, variable is rounded to the nearest 2e+10
## --------------------------------------------------------------------------------
## Lagged_Campaign_Two_Weeks
## n missing distinct Info Mean Gmd
## 5924 1105 3 0.778 1.094 0.9891
##
## Value 0 1 2
## Frequency 2542 284 3098
## Proportion 0.429 0.048 0.523
## --------------------------------------------------------------------------------
## Lagged_Campaign_General_Two_Weeks
## n missing distinct Info Sum Mean Gmd
## 3373 3656 2 0.191 3143 0.9318 0.1271
##
## --------------------------------------------------------------------------------
## Lagged_Testing_Two_Weeks
## n missing distinct Info Mean Gmd
## 5777 1252 4 0.862 0.7983 0.8687
##
## Value 0 1 2 3
## Frequency 2475 2206 882 214
## Proportion 0.428 0.382 0.153 0.037
## --------------------------------------------------------------------------------
## Lagged_Tracing_Two_Weeks
## n missing distinct Info Mean Gmd
## 5626 1403 3 0.84 0.7412 0.8734
##
## Value 0 1 2
## Frequency 2853 1376 1397
## Proportion 0.507 0.245 0.248
## --------------------------------------------------------------------------------
## Lagged_Health_Care_Two_Weeks
## n missing distinct Info Mean Gmd .05 .10
## 5451 1578 24 0.048 448467 895899 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 0
##
## lowest : 0 1 191 246961 1067577
## highest: 190372000 200000000 254591132 286175609 826000000
##
## 0 (5428, 0.996), 2000000 (2, 0.000), 4000000 (1, 0.000), 6000000 (2, 0.000),
## 8000000 (2, 0.000), 10000000 (1, 0.000), 26000000 (6, 0.001), 54000000 (1,
## 0.000), 100000000 (1, 0.000), 156000000 (1, 0.000), 174000000 (1, 0.000),
## 190000000 (1, 0.000), 200000000 (1, 0.000), 254000000 (1, 0.000), 286000000 (1,
## 0.000), 826000000 (1, 0.000)
##
## For the frequency table, variable is rounded to the nearest 2000000
## --------------------------------------------------------------------------------
## Lagged_Stringency_Index_Two_Weeks
## n missing distinct Info Mean Gmd .05 .10
## 5915 1114 289 0.954 26.61 33.22 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 11.11 53.58 79.63 87.71
##
## lowest : 0.00 2.78 3.17 3.97 5.42, highest: 94.58 94.71 96.03 97.35 100.00
## --------------------------------------------------------------------------------
## Lagged_Legacy_Stringency_Index_Two_Weeks
## n missing distinct Info Mean Gmd .05 .10
## 5917 1112 173 0.952 29.84 36.28 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 14.29 63.33 84.76 90.71
##
## lowest : 0.00 2.86 4.76 5.71 6.43, highest: 91.43 92.38 93.57 94.29 97.14
## --------------------------------------------------------------------------------
## Lagged_School_Closing_Three_Weeks
## n missing distinct Info Mean Gmd
## 5543 1486 4 0.634 0.8593 1.226
##
## Value 0 1 2 3
## Frequency 3887 77 51 1528
## Proportion 0.701 0.014 0.009 0.276
## --------------------------------------------------------------------------------
## Lagged_School_Closing_General_Three_Weeks
## n missing distinct Info Sum Mean Gmd
## 1653 5376 2 0.409 1384 0.8373 0.2727
##
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_Three_Weeks
## n missing distinct Info Mean Gmd
## 5459 1570 4 0.583 0.5708 0.9098
##
## Value 0 1 2 3
## Frequency 4069 395 264 731
## Proportion 0.745 0.072 0.048 0.134
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_General_Three_Weeks
## n missing distinct Info Sum Mean Gmd
## 1388 5641 2 0.513 1084 0.781 0.3423
##
## --------------------------------------------------------------------------------
## Lagged_Public_Events_Three_Weeks
## n missing distinct Info Mean Gmd
## 5543 1486 3 0.674 0.6112 0.8469
##
## Value 0 1 2
## Frequency 3723 252 1568
## Proportion 0.672 0.045 0.283
## --------------------------------------------------------------------------------
## Lagged_Public_Events_General_Three_Weeks
## n missing distinct Info Sum Mean Gmd
## 1800 5229 2 0.554 1360 0.7556 0.3696
##
## --------------------------------------------------------------------------------
## Lagged_Gatherings_Three_Weeks
## n missing distinct Info Mean Gmd
## 1692 5337 5 0.825 1.469 1.813
##
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##
## Value 0 1 2 3 4
## Frequency 911 81 138 119 443
## Proportion 0.538 0.048 0.082 0.070 0.262
## --------------------------------------------------------------------------------
## Lagged_Gatherings_General_Three_Weeks
## n missing distinct Info Sum Mean Gmd
## 781 6248 2 0.183 730 0.9347 0.1222
##
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_Three_Weeks
## n missing distinct Info Mean Gmd
## 5541 1488 3 0.423 0.2445 0.421
##
## Value 0 1 2
## Frequency 4610 507 424
## Proportion 0.832 0.091 0.077
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_General_Three_Weeks
## n missing distinct Info Sum Mean Gmd
## 995 6034 2 0.627 699 0.7025 0.4184
##
## --------------------------------------------------------------------------------
## Lagged_Lockdown_Three_Weeks
## n missing distinct Info Mean Gmd
## 1589 5440 4 0.763 0.7382 0.9554
##
## Value 0 1 2 3
## Frequency 947 147 459 36
## Proportion 0.596 0.093 0.289 0.023
## --------------------------------------------------------------------------------
## Lagged_Lockdown_General_Three_Weeks
## n missing distinct Info Sum Mean Gmd
## 642 6387 2 0.549 487 0.7586 0.3669
##
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_Three_Weeks
## n missing distinct Info Mean Gmd
## 5526 1503 3 0.571 0.4419 0.6852
##
## Value 0 1 2
## Frequency 4143 324 1059
## Proportion 0.750 0.059 0.192
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_General_Three_Weeks
## n missing distinct Info Sum Mean Gmd
## 1370 5659 2 0.678 897 0.6547 0.4524
##
## --------------------------------------------------------------------------------
## Lagged_International_Travel_Three_Weeks
## n missing distinct Info Mean Gmd
## 5542 1487 5 0.817 1.228 1.552
##
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##
## Value 0 1 2 3 4
## Frequency 3058 415 270 1345 454
## Proportion 0.552 0.075 0.049 0.243 0.082
## --------------------------------------------------------------------------------
## Lagged_Income_Support_Three_Weeks
## n missing distinct Info Mean Gmd
## 198 6831 2 0.116 0.08081 0.1559
##
## Value 0 2
## Frequency 190 8
## Proportion 0.96 0.04
## --------------------------------------------------------------------------------
## Lagged_Income_Support_General_Three_Weeks
## n missing distinct
## 8 7021 2
##
## Value FALSE TRUE
## Frequency 6 2
## Proportion 0.75 0.25
## --------------------------------------------------------------------------------
## Lagged_Debt_Relief_Three_Weeks
## n missing distinct Info Mean Gmd
## 198 6831 2 0.475 0.3939 0.6359
##
## Value 0 2
## Frequency 159 39
## Proportion 0.803 0.197
## --------------------------------------------------------------------------------
## Lagged_Fiscal_Measures_Three_Weeks
## n missing distinct Info Mean Gmd .05
## 5429 1600 130 0.091 1325875211 2642950468 0
## .10 .25 .50 .75 .90 .95
## 0 0 0 0 0 0
##
## lowest : 0.00 10.75 310.70 434.00 536507.00
## highest: 420453000000.00 452656000000.00 476000000000.00 990863000000.00 1957600000000.00
##
## 0 (5376, 0.990), 20000000000 (21, 0.004), 40000000000 (15, 0.003), 60000000000
## (3, 0.001), 80000000000 (3, 0.001), 120000000000 (2, 0.000), 200000000000 (1,
## 0.000), 240000000000 (2, 0.000), 360000000000 (1, 0.000), 420000000000 (1,
## 0.000), 460000000000 (1, 0.000), 480000000000 (1, 0.000), 1000000000000 (1,
## 0.000), 1960000000000 (1, 0.000)
##
## For the frequency table, variable is rounded to the nearest 2e+10
## --------------------------------------------------------------------------------
## Lagged_Campaign_Three_Weeks
## n missing distinct Info Mean Gmd
## 5542 1487 3 0.785 1.033 0.9966
##
## Value 0 1 2
## Frequency 2542 277 2723
## Proportion 0.459 0.050 0.491
## --------------------------------------------------------------------------------
## Lagged_Campaign_General_Three_Weeks
## n missing distinct Info Sum Mean Gmd
## 2991 4038 2 0.21 2765 0.9244 0.1397
##
## --------------------------------------------------------------------------------
## Lagged_Testing_Three_Weeks
## n missing distinct Info Mean Gmd
## 5414 1615 4 0.849 0.7401 0.8327
##
## Value 0 1 2 3
## Frequency 2475 2028 754 157
## Proportion 0.457 0.375 0.139 0.029
## --------------------------------------------------------------------------------
## Lagged_Tracing_Three_Weeks
## n missing distinct Info Mean Gmd
## 5268 1761 3 0.824 0.7044 0.8593
##
## Value 0 1 2
## Frequency 2803 1219 1246
## Proportion 0.532 0.231 0.237
## --------------------------------------------------------------------------------
## Lagged_Health_Care_Three_Weeks
## n missing distinct Info Mean Gmd .05 .10
## 5128 1901 24 0.051 476715 952260 0 0
## .25 .50 .75 .90 .95
## 0 0 0 0 0
##
## lowest : 0 1 191 246961 1067577
## highest: 190372000 200000000 254591132 286175609 826000000
##
## 0 (5105, 0.996), 2000000 (2, 0.000), 4000000 (1, 0.000), 6000000 (2, 0.000),
## 8000000 (2, 0.000), 10000000 (1, 0.000), 26000000 (6, 0.001), 54000000 (1,
## 0.000), 100000000 (1, 0.000), 156000000 (1, 0.000), 174000000 (1, 0.000),
## 190000000 (1, 0.000), 200000000 (1, 0.000), 254000000 (1, 0.000), 286000000 (1,
## 0.000), 826000000 (1, 0.000)
##
## For the frequency table, variable is rounded to the nearest 2000000
## --------------------------------------------------------------------------------
## Lagged_Stringency_Index_Three_Weeks
## n missing distinct Info Mean Gmd .05 .10
## 5542 1487 273 0.944 23.24 30.22 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 11.11 44.31 71.83 83.60
##
## lowest : 0.00 2.78 3.17 3.97 5.42, highest: 94.58 94.71 96.03 97.35 100.00
## --------------------------------------------------------------------------------
## Lagged_Legacy_Stringency_Index_Three_Weeks
## n missing distinct Info Mean Gmd .05 .10
## 5543 1486 170 0.942 26.3 33.45 0.00 0.00
## .25 .50 .75 .90 .95
## 0.00 14.29 53.33 80.48 88.81
##
## lowest : 0.00 2.86 4.76 5.71 6.43, highest: 90.71 92.38 93.57 94.29 97.14
## --------------------------------------------------------------------------------
## Latitude
## n missing distinct Info Mean Gmd .05 .10
## 7009 20 59 1 30.81 28.97 -35.68 -14.24
## .25 .50 .75 .90 .95
## 20.59 37.09 51.17 58.60 61.52
##
## lowest : -40.90056 -38.41610 -35.67515 -30.55948 -25.27440
## highest: 60.12816 60.47202 61.52401 61.92411 64.96305
## --------------------------------------------------------------------------------
## Longitude
## n missing distinct Info Mean Gmd .05 .10
## 7009 20 59 1 26.14 66.82 -95.713 -71.543
## .25 .50 .75 .90 .95
## 2.214 19.503 51.184 113.921 133.775
##
## lowest : -106.34677 -102.55278 -95.71289 -75.01515 -74.29733
## highest: 121.77402 127.76692 133.77514 138.25292 174.88597
## --------------------------------------------------------------------------------
## prop65
## n missing distinct Info Mean Gmd .05 .10
## 7009 20 59 1 13.83 7.266 2.550 5.123
## .25 .50 .75 .90 .95
## 8.088 14.795 19.379 21.462 21.954
##
## lowest : 1.085001 1.370070 2.550472 3.314088 3.846490
## highest: 21.655272 21.720788 21.953858 22.751680 27.576370
## --------------------------------------------------------------------------------
## propurban
## n missing distinct Info Mean Gmd .05 .10
## 7009 20 58 1 75.82 17.61 42.70 53.73
## .25 .50 .75 .90 .95
## 66.36 80.16 87.43 92.42 99.14
##
## lowest : 34.030 35.919 42.704 46.907 49.949
## highest: 93.813 94.612 98.001 99.135 100.000
## --------------------------------------------------------------------------------
## popdensity
## n missing distinct Info Mean Gmd .05 .10
## 7009 20 59 1 293 429.2 4.075 15.677
## .25 .50 .75 .90 .95
## 30.983 107.907 232.172 377.215 511.458
##
## lowest : 3.249129 3.526923 4.075308 8.822080 14.554920
## highest: 454.938073 511.457910 529.652104 1511.031250 7952.998418
## --------------------------------------------------------------------------------
## driving
## n missing distinct Info Mean Gmd .05 .10
## 5512 1517 4248 1 82.68 39.91 20.77 30.58
## .25 .50 .75 .90 .95
## 51.53 94.91 108.66 122.17 131.36
##
## lowest : 8.74 9.82 9.92 10.08 10.76, highest: 170.46 172.10 173.18 175.25 186.34
## --------------------------------------------------------------------------------
## walking
## n missing distinct Info Mean Gmd .05 .10
## 5512 1517 4456 1 82.26 46.33 18.82 25.97
## .25 .50 .75 .90 .95
## 45.00 91.76 110.77 130.01 143.37
##
## lowest : 5.82 6.27 6.30 6.67 6.96, highest: 220.80 229.51 235.37 236.75 362.50
## --------------------------------------------------------------------------------
## transit
## n missing distinct Info Mean Gmd .05 .10
## 2756 4273 2348 1 72.91 45.86 11.95 16.79
## .25 .50 .75 .90 .95
## 28.67 91.09 106.72 117.87 125.75
##
## lowest : 7.04 7.16 7.33 7.58 7.61, highest: 155.72 157.63 157.64 160.26 160.56
## --------------------------------------------------------------------------------
## arrivals
## n missing distinct Info Mean Gmd .05 .10
## 7029 0 59 1 19140656 19955221 2256000 2825000
## .25 .50 .75 .90 .95
## 4425000 11196000 24551000 45768000 79745920
##
## lowest : 1018000 1819300 1946000 2256000 2343800
## highest: 61567200 62900000 79745920 82773000 89322000
## --------------------------------------------------------------------------------
## departures
## n missing distinct Info Mean Gmd .05 .10
## 5949 1080 50 1 19130811 22827618 1368000 1923000
## .25 .50 .75 .90 .95
## 3188000 9889000 19748000 41964000 92564000
##
## lowest : 667000 668000 1368000 1446000 1501000
## highest: 41964000 70386000 92564000 108542000 149720000
## --------------------------------------------------------------------------------
## vul_emp
## n missing distinct Info Mean Gmd .05 .10
## 7029 0 59 1 17.21 15.59 1.083 4.895
## .25 .50 .75 .90 .95
## 7.385 10.691 21.805 47.538 50.418
##
## lowest : 0.144 0.932 1.083 3.016 3.837, highest: 47.895 48.472 50.418 54.074 74.270
## --------------------------------------------------------------------------------
## GNI
## n missing distinct Info Mean Gmd .05 .10
## 7029 0 59 1 30183 25091 2800 3840
## .25 .50 .75 .90 .95
## 10230 24580 47090 61390 70870
##
## lowest : 2020 2360 2800 3090 3830, highest: 63080 67960 70870 80610 84410
## --------------------------------------------------------------------------------
## health_exp
## n missing distinct Info Mean Gmd .05 .10
## 7029 0 59 1 2580 2587 115.0 161.0
## .25 .50 .75 .90 .95
## 499.2 1529.1 4379.7 5800.2 7936.4
##
## lowest : 69.2931 105.7685 114.9718 129.5760 132.9010
## highest: 5904.5840 6086.3115 7936.3750 9956.2598 10246.1387
## --------------------------------------------------------------------------------
## pollution
## n missing distinct Info Mean Gmd .05 .10
## 7029 0 59 1 22.45 18.9 6.185 6.732
## .25 .50 .75 .90 .95
## 10.365 16.036 24.787 52.665 87.945
##
## lowest : 5.861331 5.956001 6.184665 6.428383 6.481147
## highest: 60.745160 86.999452 87.945447 90.873210 91.187328
## --------------------------------------------------------------------------------
## EUI_democracy
## n missing distinct Info Mean Gmd .05 .10
## 7029 0 56 1 7.015 2.181 2.76 3.11
## .25 .50 .75 .90 .95
## 6.48 7.50 8.29 9.24 9.39
##
## lowest : 1.93 2.26 2.76 3.06 3.08, highest: 9.25 9.26 9.39 9.58 9.87
## --------------------------------------------------------------------------------
## freedom_house
## n missing distinct Info Mean Gmd .05 .10
## 7029 0 37 0.998 73.56 29.39 17 21
## .25 .50 .75 .90 .95
## 59 88 94 98 100
##
## lowest : 7 10 17 20 21, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## cellular_sub
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 58 1 126.7 24.17 89.58 97.30
## .25 .50 .75 .90 .95
## 115.53 126.20 134.93 157.43 171.61
##
## lowest : 86.94256 87.62149 89.58002 95.23160 95.28660
## highest: 159.93066 163.87035 171.60533 180.18261 208.50483
## --------------------------------------------------------------------------------
## milex
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 56 1 24533673 39294131 -9 329000
## .25 .50 .75 .90 .95
## 1762000 4858000 13927000 56724000 61274000
##
## lowest : -9 50000 256000 267000 329000
## highest: 58765000 59077000 61274000 102643000 655388000
## --------------------------------------------------------------------------------
## milper
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 53 1 216.1 301.6 5 10
## .25 .50 .75 .90 .95
## 20 73 239 482 1325
##
## lowest : 0 1 2 5 6, highest: 511 956 1325 1569 2285
## --------------------------------------------------------------------------------
## irst
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 51 0.997 24326 41544 0 0
## .25 .50 .75 .90 .95
## 805 3759 8366 42661 88695
##
## lowest : 0 150 300 539 632, highest: 70209 77264 88695 107232 731040
##
## Value 0 2000 4000 6000 8000 10000 14000 16000 18000
## Frequency 1990 1320 720 840 360 120 239 120 120
## Proportion 0.288 0.191 0.104 0.122 0.052 0.017 0.035 0.017 0.017
##
## Value 28000 34000 36000 42000 70000 78000 88000 108000 732000
## Frequency 120 120 120 120 120 120 120 120 120
## Proportion 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017
##
## For the frequency table, variable is rounded to the nearest 2000
## --------------------------------------------------------------------------------
## pec
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 58 1 315019 478868 2776 5653
## .25 .50 .75 .90 .95
## 27817 86845 234432 468740 1385461
##
## lowest : 283 590 2261 2776 2937
## highest: 737482 1356742 1385461 3159873 5333707
## --------------------------------------------------------------------------------
## cinc
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 58 1 0.01385 0.02086 0.0001812 0.0003069
## .25 .50 .75 .90 .95
## 0.0014276 0.0038830 0.0098926 0.0250626 0.0808987
##
## lowest : 0.0000308 0.0000341 0.0001778 0.0001812 0.0002811
## highest: 0.0355880 0.0400789 0.0808987 0.1393526 0.2181166
## --------------------------------------------------------------------------------
## endocrine_deaths
## n missing distinct Info Mean Gmd .05 .10
## 1560 5469 13 0.994 5797 8566 1 2
## .25 .50 .75 .90 .95
## 98 346 8481 25482 27148
##
## lowest : 1 2 10 98 99, highest: 1774 8481 10564 25482 27148
##
## Value 1 2 10 98 99 329 346 1024 1774 8481 10564
## Frequency 120 120 120 120 120 120 120 120 120 120 120
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##
## Value 25482 27148
## Frequency 120 120
## Proportion 0.077 0.077
## --------------------------------------------------------------------------------
## blood_pressure_deaths
## n missing distinct Info Mean Gmd .05 .10
## 5469 1560 46 1 6759 9535 8 35
## .25 .50 .75 .90 .95
## 164 2310 7559 18681 22129
##
## lowest : 1 4 8 14 35, highest: 18681 19729 22129 42920 74902
## --------------------------------------------------------------------------------
## respiratory_deaths
## n missing distinct Info Mean Gmd .05 .10
## 5589 1440 47 1 68167 92521 682 826
## .25 .50 .75 .90 .95
## 3865 23856 82599 170447 201122
##
## lowest : 603 611 682 712 826, highest: 170447 186220 201122 463195 606604
## --------------------------------------------------------------------------------
## Lagged_Mortality_One_Week
## n missing distinct Info Mean Gmd
## 5371 1658 2338 0.838 0.00001034 0.00001936
## .05 .10 .25 .50 .75 .90
## 0.000000000 0.000000000 0.000000000 0.000000000 0.000001219 0.000012357
## .95
## 0.000046166
##
## lowest : 0.0000000000000000 0.0000000001035193 0.0000000002070387 0.0000000004140774 0.0000000004315891
## highest: 0.0005225490522846 0.0005405947629573 0.0005579871794594 0.0005749851541981 0.0005914161189021
## --------------------------------------------------------------------------------
## Lagged_Mortality_Two_Weeks
## n missing distinct Info Mean Gmd
## 4997 2032 2020 0.806 0.000008176 0.00001547
## .05 .10 .25 .50 .75 .90
## 0.000000000 0.000000000 0.000000000 0.000000000 0.000000667 0.000008019
## .95
## 0.000030705
##
## lowest : 0.0000000000000000 0.0000000001035193 0.0000000002070387 0.0000000004140774 0.0000000004315891
## highest: 0.0005225490522846 0.0005405947629573 0.0005579871794594 0.0005749851541981 0.0005914161189021
## --------------------------------------------------------------------------------
## Lagged_Mortality_Three_Weeks
## n missing distinct Info Mean Gmd
## 4590 2439 1678 0.762 0.000006605 0.00001262
## .05 .10 .25 .50 .75 .90
## 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000003117 0.0000044149
## .95
## 0.0000232602
##
## lowest : 0.0000000000000000 0.0000000001035193 0.0000000002070387 0.0000000004140774 0.0000000004315891
## highest: 0.0005225490522846 0.0005405947629573 0.0005579871794594 0.0005749851541981 0.0005914161189021
## --------------------------------------------------------------------------------
## Lagged_Mortality_Four_Weeks
## n missing distinct Info Mean Gmd
## 4181 2848 1354 0.709 0.000006095 0.00001174
## .05 .10 .25 .50 .75 .90
## 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000001159 0.0000032705
## .95
## 0.0000163414
##
## lowest : 0.0000000000000000 0.0000000001035193 0.0000000002070387 0.0000000004140774 0.0000000004315891
## highest: 0.0005225490522846 0.0005405947629573 0.0005579871794594 0.0005749851541981 0.0005914161189021
## --------------------------------------------------------------------------------
## govt_accountability
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 52 0.999 0.5407 1.065 -1.45 -1.11
## .25 .50 .75 .90 .95
## -0.01 0.86 1.38 1.61 1.62
##
## lowest : -1.64 -1.45 -1.28 -1.20 -1.11, highest: 1.57 1.60 1.61 1.62 1.73
## --------------------------------------------------------------------------------
## political_stability
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 52 1 0.3303 0.8518 -1.12 -0.81
## .25 .50 .75 .90 .95
## -0.28 0.43 0.92 1.29 1.41
##
## lowest : -1.33 -1.16 -1.12 -0.96 -0.93, highest: 1.34 1.37 1.41 1.51 1.54
## --------------------------------------------------------------------------------
## govt_effectiveness
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 54 1 0.8903 0.8495 -0.25 -0.09
## .25 .50 .75 .90 .95
## 0.28 1.07 1.48 1.85 1.98
##
## lowest : -0.58 -0.45 -0.25 -0.21 -0.15, highest: 1.87 1.89 1.98 2.04 2.23
## --------------------------------------------------------------------------------
## regulatory_quality
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 52 0.999 0.8599 0.9027 -0.39 -0.24
## .25 .50 .75 .90 .95
## 0.11 0.93 1.58 1.79 1.98
##
## lowest : -0.87 -0.54 -0.39 -0.31 -0.24, highest: 1.80 1.93 1.98 2.02 2.13
## --------------------------------------------------------------------------------
## rule_of_law
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 52 1 0.7956 0.9739 -0.52 -0.41
## .25 .50 .75 .90 .95
## 0.02 0.97 1.63 1.88 1.93
##
## lowest : -0.82 -0.67 -0.52 -0.48 -0.41, highest: 1.88 1.90 1.93 1.97 2.05
## --------------------------------------------------------------------------------
## control_of_corruption
## n missing distinct Info Mean Gmd .05 .10
## 6909 120 50 0.999 0.734 1.097 -0.59 -0.49
## .25 .50 .75 .90 .95
## -0.19 0.61 1.60 2.09 2.17
##
## lowest : -0.86 -0.85 -0.59 -0.54 -0.49, highest: 2.09 2.14 2.15 2.17 2.21
## --------------------------------------------------------------------------------
##
## Variables with all observations missing:
##
## [1] M1_Wildcard
# n, nmiss, unique, mean, 5, 10, 25, 50, 75, 90, 95th percentiles
# Next, I generate shareable outputs, using the summarytools package. Set st_options(use.x11 = FALSE).
saved_x11_option <- st_options("use.x11")
st_options(use.x11 = TRUE)
dfSummary(Final_Data_Country, plain.ascii = FALSE, style = "grid",
graph.magnif = 0.75, valid.col = FALSE, tmp.img.dir = "/tmp")
## ### Data Frame Summary
## #### Final_Data_Country
## **Dimensions:** 7029 x 160
## **Duplicates:** 0
##
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | No | Variable | Stats / Values | Freqs (% of Valid) | Graph | Missing |
## +=====+===============================================+=========================================+======================+======================+==========+
## | 1 | COUNTRY\ | 1\. Argentina\ | 120 ( 1.7%)\ |  | 0\ |
## | | [character] | 2\. Australia\ | 120 ( 1.7%)\ | | (0%) |
## | | | 3\. Austria\ | 120 ( 1.7%)\ | | |
## | | | 4\. Belgium\ | 120 ( 1.7%)\ | | |
## | | | 5\. Brazil\ | 120 ( 1.7%)\ | | |
## | | | 6\. Canada\ | 120 ( 1.7%)\ | | |
## | | | 7\. Chile\ | 120 ( 1.7%)\ | | |
## | | | 8\. China\ | 120 ( 1.7%)\ | | |
## | | | 9\. Colombia\ | 120 ( 1.7%)\ | | |
## | | | 10\. Cyprus\ | 120 ( 1.7%)\ | | |
## | | | [ 49 others ] | 5829 (82.9%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 2 | X1\ | Mean (sd) : 3515 (2029.2)\ | 7029 distinct values |  | 0\ |
## | | [numeric] | min < med < max:\ | | | (0%) |
## | | | 1 < 3515 < 7029\ | | | |
## | | | IQR (CV) : 3514 (0.6) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 3 | Date\ | min : 2020-01-01\ | 120 distinct values |  | 0\ |
## | | [Date] | med : 2020-03-01\ | | | (0%) |
## | | | max : 2020-04-29\ | | | |
## | | | range : 3m 28d | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 4 | Total_Cases_Country\ | Mean (sd) : 10836.6 (52678.8)\ | 2459 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 0 < 98 < 1012582\ | | | |
## | | | IQR (CV) : 2620 (4.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 5 | Total_Deceased_Country\ | Mean (sd) : 690.8 (3553.1)\ | 911 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 0 < 0 < 58355\ | | | |
## | | | IQR (CV) : 50 (5.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 6 | New_Confirmed_Country\ | Mean (sd) : 501.3 (2407.7)\ | 1090 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 0 < 8 < 36188\ | | | |
## | | | IQR (CV) : 133.8 (4.8) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 7 | New_Total_Deceased_Country\ | Mean (sd) : 36 (175.2)\ | 375 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 0 < 0 < 2584\ | | | |
## | | | IQR (CV) : 4 (4.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 8 | Population\ | Mean (sd) : 92628187.3 (254800293.8)\ | 59 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 341250 < 19116209 < 1439323774\ | | | |
## | | | IQR (CV) : 62345286 (2.8) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 9 | Total_Mortality_Rate_Per_Capita\ | Mean (sd) : 0 (0)\ | 2120 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 0 < 0 < 0\ | | | |
## | | | IQR (CV) : 0 (3.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 10 | New_Mortality_Rate_Per_Capita\ | Mean (sd) : 0 (0)\ | 1155 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 0 < 0 < 0\ | | | |
## | | | IQR (CV) : 0 (3.5) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 11 | Total_Cases_Country_Per_Capita\ | Mean (sd) : 0 (0)\ | 3450 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 0 < 0 < 0\ | | | |
## | | | IQR (CV) : 0 (2.6) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 12 | rolling_average_confirmed\ | Mean (sd) : 464.3 (2262.9)\ | 1971 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 0 < 7.6 < 31595.4\ | | | |
## | | | IQR (CV) : 120.1 (4.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 13 | rolling_average_deceased\ | Mean (sd) : 33.4 (164.3)\ | 702 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 0 < 0 < 2201.6\ | | | |
## | | | IQR (CV) : 3.1 (4.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 14 | total_rolling_average_mortality\ | Mean (sd) : 0 (0)\ | 2611 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 0 < 0 < 0\ | | | |
## | | | IQR (CV) : 0 (4.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 15 | new_rolling_average_mortality\ | Mean (sd) : 0 (0)\ | 1824 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | 0 < 0 < 0\ | | | |
## | | | IQR (CV) : 0 (3.4) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 16 | latitude\ | Mean (sd) : 31 (27)\ | 59 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | -40.9 < 37.1 < 65\ | | | |
## | | | IQR (CV) : 30.6 (0.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 17 | longitude\ | Mean (sd) : 26 (61)\ | 59 distinct values |  | 1319\ |
## | | [numeric] | min < med < max:\ | | | (18.77%) |
## | | | -106.3 < 19.5 < 174.9\ | | | |
## | | | IQR (CV) : 49 (2.3) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 18 | C1_School.closing\ | Mean (sd) : 1.2 (1.4)\ | 0 : 3887 (59.0%)\ |  | 436\ |
## | | [numeric] | min < med < max:\ | 1 : 117 ( 1.8%)\ | | (6.2%) |
## | | | 0 < 0 < 3\ | 2 : 144 ( 2.2%)\ | | |
## | | | IQR (CV) : 3 (1.2) | 3 : 2445 (37.1%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 19 | C1_Flag\ | Min : 0\ | 0 : 371 (13.8%)\ |  | 4330\ |
## | | [numeric] | Mean : 0.9\ | 1 : 2328 (86.2%) | | (61.6%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 20 | C2_Workplace.closing\ | Mean (sd) : 0.9 (1.2)\ | 0 : 4086 (62.8%)\ |  | 524\ |
## | | [numeric] | min < med < max:\ | 1 : 546 ( 8.4%)\ | | (7.45%) |
## | | | 0 < 0 < 3\ | 2 : 523 ( 8.0%)\ | | |
## | | | IQR (CV) : 2 (1.4) | 3 : 1350 (20.8%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 21 | C2_Flag\ | Min : 0\ | 0 : 445 (18.4%)\ |  | 4612\ |
## | | [numeric] | Mean : 0.8\ | 1 : 1972 (81.6%) | | (65.61%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 22 | C3_Cancel.public.events\ | Mean (sd) : 0.8 (1)\ | 0 : 3723 (56.8%)\ |  | 479\ |
## | | [numeric] | min < med < max:\ | 1 : 279 ( 4.3%)\ | | (6.81%) |
## | | | 0 < 0 < 2\ | 2 : 2548 (38.9%) | | |
## | | | IQR (CV) : 2 (1.2) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 23 | C3_Flag\ | Min : 0\ | 0 : 543 (19.3%)\ |  | 4213\ |
## | | [numeric] | Mean : 0.8\ | 1 : 2273 (80.7%) | | (59.94%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 24 | C4_Restrictions.on.gatherings\ | Mean (sd) : 2.1 (1.8)\ | 0 : 944 (37.4%)\ |  | 4507\ |
## | | [numeric] | min < med < max:\ | 1 : 140 ( 5.5%)\ | | (64.12%) |
## | | | 0 < 2 < 4\ | 2 : 224 ( 8.9%)\ | | |
## | | | IQR (CV) : 4 (0.9) | 3 : 236 ( 9.4%)\ | | |
## | | | | 4 : 978 (38.8%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 25 | C4_Flag\ | Min : 0\ | 0 : 137 ( 8.7%)\ |  | 5451\ |
## | | [numeric] | Mean : 0.9\ | 1 : 1441 (91.3%) | | (77.55%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 26 | C5_Close.public.transport\ | Mean (sd) : 0.4 (0.7)\ | 0 : 4901 (74.9%)\ |  | 482\ |
## | | [numeric] | min < med < max:\ | 1 : 877 (13.4%)\ | | (6.86%) |
## | | | 0 < 0 < 2\ | 2 : 769 (11.8%) | | |
## | | | IQR (CV) : 1 (1.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 27 | C5_Flag\ | Min : 0\ | 0 : 423 (24.7%)\ |  | 5319\ |
## | | [numeric] | Mean : 0.8\ | 1 : 1287 (75.3%) | | (75.67%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 28 | C6_Stay.at.home.requirements\ | Mean (sd) : 1.1 (1)\ | 0 : 1033 (42.6%)\ |  | 4604\ |
## | | [numeric] | min < med < max:\ | 1 : 321 (13.2%)\ | | (65.5%) |
## | | | 0 < 1 < 3\ | 2 : 950 (39.2%)\ | | |
## | | | IQR (CV) : 2 (0.9) | 3 : 121 ( 5.0%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 29 | C6_Flag\ | Min : 0\ | 0 : 292 (21.0%)\ |  | 5637\ |
## | | [numeric] | Mean : 0.8\ | 1 : 1100 (79.0%) | | (80.2%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 30 | C7_Restrictions.on.internal.movement\ | Mean (sd) : 0.6 (0.9)\ | 0 : 4205 (64.5%)\ |  | 506\ |
## | | [numeric] | min < med < max:\ | 1 : 477 ( 7.3%)\ | | (7.2%) |
## | | | 0 < 0 < 2\ | 2 : 1841 (28.2%) | | |
## | | | IQR (CV) : 2 (1.4) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 31 | C7_Flag\ | Min : 0\ | 0 : 652 (28.3%)\ |  | 4727\ |
## | | [numeric] | Mean : 0.7\ | 1 : 1650 (71.7%) | | (67.25%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 32 | C8_International.travel.controls\ | Mean (sd) : 1.5 (1.6)\ | 0 : 3094 (47.1%)\ |  | 456\ |
## | | [numeric] | min < med < max:\ | 1 : 415 ( 6.3%)\ | | (6.49%) |
## | | | 0 < 1 < 4\ | 2 : 375 ( 5.7%)\ | | |
## | | | IQR (CV) : 3 (1) | 3 : 1733 (26.4%)\ | | |
## | | | | 4 : 956 (14.5%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 33 | E1_Income.support\ | Min : 0\ | 0 : 209 (87.8%)\ |  | 6791\ |
## | | [numeric] | Mean : 0.2\ | 2 : 29 (12.2%) | | (96.61%) |
## | | | Max : 2 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 34 | E1_Flag\ | 1\. FALSE\ | 27 (93.1%)\ |  | 7000\ |
## | | [logical] | 2\. TRUE | 2 ( 6.9%) | | (99.59%) |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 35 | E2_Debt.contract.relief\ | Mean (sd) : 0.6 (0.9)\ | 0 : 161 (67.1%)\ |  | 6789\ |
## | | [numeric] | min < med < max:\ | 1 : 19 ( 7.9%)\ | | (96.59%) |
## | | | 0 < 0 < 2\ | 2 : 60 (25.0%) | | |
## | | | IQR (CV) : 1.2 (1.5) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 36 | E3_Fiscal.measures\ | Mean (sd) : 1240873079 (31161625396.3)\ | 154 distinct values |  | 1023\ |
## | | [numeric] | min < med < max:\ | | | (14.55%) |
## | | | 0 < 0 < 1957600000000\ | | | |
## | | | IQR (CV) : 0 (25.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 37 | H1_Public.information.campaigns\ | Mean (sd) : 1.2 (1)\ | 0 : 2542 (38.9%)\ |  | 486\ |
## | | [numeric] | min < med < max:\ | 1 : 302 ( 4.6%)\ | | (6.91%) |
## | | | 0 < 2 < 2\ | 2 : 3699 (56.5%) | | |
## | | | IQR (CV) : 2 (0.8) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 38 | H1_Flag\ | Min : 0\ | 0 : 230 ( 5.8%)\ |  | 3037\ |
## | | [numeric] | Mean : 0.9\ | 1 : 3762 (94.2%) | | (43.21%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 39 | H2_Testing.policy\ | Mean (sd) : 0.8 (0.9)\ | 0 : 2475 (41.1%)\ |  | 1003\ |
## | | [numeric] | min < med < max:\ | 1 : 2298 (38.1%)\ | | (14.27%) |
## | | | 0 < 1 < 3\ | 2 : 976 (16.2%)\ | | |
## | | | IQR (CV) : 1 (1) | 3 : 277 ( 4.6%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 40 | H3_Contact.tracing\ | Mean (sd) : 0.8 (0.8)\ | 0 : 2890 (49.4%)\ |  | 1174\ |
## | | [numeric] | min < med < max:\ | 1 : 1455 (24.9%)\ | | (16.7%) |
## | | | 0 < 1 < 2\ | 2 : 1510 (25.8%) | | |
## | | | IQR (CV) : 2 (1.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 41 | H4_Emergency.investment.in.healthcare\ | Mean (sd) : 82850864.3 (3384818849.3)\ | 89 distinct values |  | 1230\ |
## | | [numeric] | min < med < max:\ | | | (17.5%) |
## | | | 0 < 0 < 242400000000\ | | | |
## | | | IQR (CV) : 0 (40.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 42 | H5_Investment.in.vaccines\ | Mean (sd) : 434842.1 (13135239)\ | 25 distinct values |  | 1367\ |
## | | [numeric] | min < med < max:\ | | | (19.45%) |
## | | | 0 < 0 < 826000000\ | | | |
## | | | IQR (CV) : 0 (30.2) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 43 | M1_Wildcard\ | All NA's | | | 7029\ |
## | | [logical] | | | | (100%) |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 44 | StringencyIndex\ | Mean (sd) : 31.4 (33.8)\ | 323 distinct values |  | 535\ |
## | | [numeric] | min < med < max:\ | | | (7.61%) |
## | | | 0 < 13.9 < 100\ | | | |
## | | | IQR (CV) : 64 (1.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 45 | LegacyStringencyIndex\ | Mean (sd) : 34.5 (35.8)\ | 183 distinct values |  | 532\ |
## | | [numeric] | min < med < max:\ | | | (7.57%) |
## | | | 0 < 17.1 < 97.1\ | | | |
## | | | IQR (CV) : 74.5 (1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 46 | Oxford_Cases\ | Mean (sd) : 10841.2 (52728.7)\ | 2408 distinct values |  | 1399\ |
## | | [numeric] | min < med < max:\ | | | (19.9%) |
## | | | 0 < 83 < 1012583\ | | | |
## | | | IQR (CV) : 2781.5 (4.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 47 | Oxford_Deaths\ | Mean (sd) : 687.9 (3524)\ | 907 distinct values |  | 1399\ |
## | | [numeric] | min < med < max:\ | | | (19.9%) |
## | | | 0 < 0 < 58355\ | | | |
## | | | IQR (CV) : 53 (5.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 48 | Lagged_School_Closing_One_Week\ | Mean (sd) : 1.1 (1.4)\ | 0 : 3887 (61.5%)\ |  | 707\ |
## | | [numeric] | min < med < max:\ | 1 : 105 ( 1.7%)\ | | (10.06%) |
## | | | 0 < 0 < 3\ | 2 : 104 ( 1.7%)\ | | |
## | | | IQR (CV) : 3 (1.3) | 3 : 2226 (35.2%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 49 | Lagged_School_Closing_General_One_Week\ | Min : 0\ | 0 : 347 (14.3%)\ |  | 4601\ |
## | | [numeric] | Mean : 0.9\ | 1 : 2081 (85.7%) | | (65.46%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 50 | Lagged_Workplace_Closing_One_Week\ | Mean (sd) : 0.8 (1.2)\ | 0 : 4083 (65.5%)\ |  | 795\ |
## | | [numeric] | min < med < max:\ | 1 : 508 ( 8.2%)\ | | (11.31%) |
## | | | 0 < 0 < 3\ | 2 : 436 ( 7.0%)\ | | |
## | | | IQR (CV) : 2 (1.5) | 3 : 1207 (19.4%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 51 | Lagged_Workplace_Closing_General_One_Week\ | Min : 0\ | 0 : 409 (19.0%)\ |  | 4880\ |
## | | [numeric] | Mean : 0.8\ | 1 : 1740 (81.0%) | | (69.43%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 52 | Lagged_Public_Events_One_Week\ | Mean (sd) : 0.8 (1)\ | 0 : 3723 (59.1%)\ |  | 724\ |
## | | [numeric] | min < med < max:\ | 1 : 269 ( 4.3%)\ | | (10.3%) |
## | | | 0 < 0 < 2\ | 2 : 2313 (36.7%) | | |
## | | | IQR (CV) : 2 (1.2) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 53 | Lagged_Public_Events_General_One_Week\ | Min : 0\ | 0 : 524 (20.4%)\ |  | 4462\ |
## | | [numeric] | Mean : 0.8\ | 1 : 2043 (79.6%) | | (63.48%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 54 | Lagged_Gatherings_One_Week\ | Mean (sd) : 2 (1.8)\ | 0 : 933 (40.6%)\ |  | 4733\ |
## | | [numeric] | min < med < max:\ | 1 : 124 ( 5.4%)\ | | (67.34%) |
## | | | 0 < 2 < 4\ | 2 : 202 ( 8.8%)\ | | |
## | | | IQR (CV) : 4 (0.9) | 3 : 196 ( 8.5%)\ | | |
## | | | | 4 : 841 (36.6%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 55 | Lagged_Gatherings_General_One_Week\ | Min : 0\ | 0 : 116 ( 8.5%)\ |  | 5666\ |
## | | [numeric] | Mean : 0.9\ | 1 : 1247 (91.5%) | | (80.61%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 56 | Lagged_Public_Transport_One_Week\ | Mean (sd) : 0.3 (0.7)\ | 0 : 4836 (76.7%)\ |  | 723\ |
## | | [numeric] | min < med < max:\ | 1 : 786 (12.5%)\ | | (10.29%) |
## | | | 0 < 0 < 2\ | 2 : 684 (10.8%) | | |
## | | | IQR (CV) : 0 (1.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 57 | Lagged_Public_Transport_General_One_Week\ | Min : 0\ | 0 : 398 (25.9%)\ |  | 5495\ |
## | | [numeric] | Mean : 0.7\ | 1 : 1136 (74.1%) | | (78.18%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 58 | Lagged_Lockdown_One_Week\ | Mean (sd) : 1 (1)\ | 0 : 1004 (45.8%)\ |  | 4835\ |
## | | [numeric] | min < med < max:\ | 1 : 256 (11.7%)\ | | (68.79%) |
## | | | 0 < 1 < 3\ | 2 : 835 (38.1%)\ | | |
## | | | IQR (CV) : 2 (1) | 3 : 99 ( 4.5%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 59 | Lagged_Lockdown_General_One_Week\ | Min : 0\ | 0 : 264 (22.2%)\ |  | 5839\ |
## | | [numeric] | Mean : 0.8\ | 1 : 926 (77.8%) | | (83.07%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 60 | Lagged_Internal_Movement_One_Week\ | Mean (sd) : 0.6 (0.9)\ | 0 : 4185 (66.6%)\ |  | 744\ |
## | | [numeric] | min < med < max:\ | 1 : 426 ( 6.8%)\ | | (10.58%) |
## | | | 0 < 0 < 2\ | 2 : 1674 (26.6%) | | |
## | | | IQR (CV) : 2 (1.5) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 61 | Lagged_Internal_Movement_General_One_Week\ | Min : 0\ | 0 : 625 (30.0%)\ |  | 4945\ |
## | | [numeric] | Mean : 0.7\ | 1 : 1459 (70.0%) | | (70.35%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 62 | Lagged_International_Travel_One_Week\ | Mean (sd) : 1.5 (1.6)\ | 0 : 3086 (48.9%)\ |  | 713\ |
## | | [numeric] | min < med < max:\ | 1 : 415 ( 6.6%)\ | | (10.14%) |
## | | | 0 < 1 < 4\ | 2 : 346 ( 5.5%)\ | | |
## | | | IQR (CV) : 3 (1.1) | 3 : 1658 (26.2%)\ | | |
## | | | | 4 : 811 (12.8%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 63 | Lagged_Income_Support_One_Week\ | Min : 0\ | 0 : 204 (90.3%)\ |  | 6803\ |
## | | [numeric] | Mean : 0.2\ | 2 : 22 ( 9.7%) | | (96.78%) |
## | | | Max : 2 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 64 | Lagged_Income_Support_General_One_Week\ | 1\. FALSE\ | 20 (90.9%)\ |  | 7007\ |
## | | [logical] | 2\. TRUE | 2 ( 9.1%) | | (99.69%) |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 65 | Lagged_Debt_Relief_One_Week\ | Mean (sd) : 0.5 (0.8)\ | 0 : 161 (71.2%)\ |  | 6803\ |
## | | [numeric] | min < med < max:\ | 1 : 12 ( 5.3%)\ | | (96.78%) |
## | | | 0 < 0 < 2\ | 2 : 53 (23.4%) | | |
## | | | IQR (CV) : 1 (1.6) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 66 | Lagged_Fiscal_Measures_One_Week\ | Mean (sd) : 1246051987.5 | 153 distinct values |  | 1048\ |
## | | [numeric] | (31226591440.9)\ | | | (14.91%) |
## | | | min < med < max:\ | | | |
## | | | 0 < 0 < 1957600000000\ | | | |
## | | | IQR (CV) : 0 (25.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 67 | Lagged_Campaign_One_Week\ | Mean (sd) : 1.1 (1)\ | 0 : 2542 (40.3%)\ |  | 720\ |
## | | [numeric] | min < med < max:\ | 1 : 296 ( 4.7%)\ | | (10.24%) |
## | | | 0 < 2 < 2\ | 2 : 3471 (55.0%) | | |
## | | | IQR (CV) : 2 (0.8) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 68 | Lagged_Campaign_General_One_Week\ | Min : 0\ | 0 : 230 ( 6.1%)\ |  | 3271\ |
## | | [numeric] | Mean : 0.9\ | 1 : 3528 (93.9%) | | (46.54%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 69 | Lagged_Testing_One_Week\ | Mean (sd) : 0.8 (0.8)\ | 0 : 2475 (41.3%)\ |  | 1033\ |
## | | [numeric] | min < med < max:\ | 1 : 2289 (38.2%)\ | | (14.7%) |
## | | | 0 < 1 < 3\ | 2 : 971 (16.2%)\ | | |
## | | | IQR (CV) : 1 (1) | 3 : 261 ( 4.3%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 70 | Lagged_Tracing_One_Week\ | Mean (sd) : 0.8 (0.8)\ | 0 : 2889 (49.5%)\ |  | 1193\ |
## | | [numeric] | min < med < max:\ | 1 : 1451 (24.9%)\ | | (16.97%) |
## | | | 0 < 1 < 2\ | 2 : 1496 (25.6%) | | |
## | | | IQR (CV) : 2 (1.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 71 | Lagged_Health_Care_One_Week\ | Mean (sd) : 436074.4 (13153820.4)\ | 25 distinct values |  | 1383\ |
## | | [numeric] | min < med < max:\ | | | (19.68%) |
## | | | 0 < 0 < 826000000\ | | | |
## | | | IQR (CV) : 0 (30.2) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 72 | Lagged_Stringency_Index_One_Week\ | Mean (sd) : 29.8 (33.1)\ | 320 distinct values |  | 733\ |
## | | [numeric] | min < med < max:\ | | | (10.43%) |
## | | | 0 < 11.1 < 100\ | | | |
## | | | IQR (CV) : 61.3 (1.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 73 | Lagged_Legacy_Stringency_Index_One_Week\ | Mean (sd) : 33 (35.2)\ | 181 distinct values |  | 731\ |
## | | [numeric] | min < med < max:\ | | | (10.4%) |
## | | | 0 < 14.3 < 97.1\ | | | |
## | | | IQR (CV) : 70.5 (1.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 74 | Lagged_School_Closing_Two_Weeks\ | Mean (sd) : 1 (1.4)\ | 0 : 3887 (65.5%)\ |  | 1097\ |
## | | [numeric] | min < med < max:\ | 1 : 91 ( 1.5%)\ | | (15.61%) |
## | | | 0 < 0 < 3\ | 2 : 59 ( 1.0%)\ | | |
## | | | IQR (CV) : 3 (1.4) | 3 : 1895 (31.9%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 75 | Lagged_School_Closing_General_Two_Weeks\ | Min : 0\ | 0 : 305 (15.0%)\ |  | 4991\ |
## | | [numeric] | Mean : 0.9\ | 1 : 1733 (85.0%) | | (71.01%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 76 | Lagged_Workplace_Closing_Two_Weeks\ | Mean (sd) : 0.7 (1.2)\ | 0 : 4080 (69.8%)\ |  | 1183\ |
## | | [numeric] | min < med < max:\ | 1 : 457 ( 7.8%)\ | | (16.83%) |
## | | | 0 < 0 < 3\ | 2 : 336 ( 5.8%)\ | | |
## | | | IQR (CV) : 1 (1.7) | 3 : 973 (16.6%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 77 | Lagged_Workplace_Closing_General_Two_Weeks\ | Min : 0\ | 0 : 351 (19.9%)\ |  | 5265\ |
## | | [numeric] | Mean : 0.8\ | 1 : 1413 (80.1%) | | (74.9%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 78 | Lagged_Public_Events_Two_Weeks\ | Mean (sd) : 0.7 (0.9)\ | 0 : 3723 (62.8%)\ |  | 1104\ |
## | | [numeric] | min < med < max:\ | 1 : 259 ( 4.4%)\ | | (15.71%) |
## | | | 0 < 0 < 2\ | 2 : 1943 (32.8%) | | |
## | | | IQR (CV) : 2 (1.3) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 79 | Lagged_Public_Events_General_Two_Weeks\ | Min : 0\ | 0 : 482 (22.1%)\ |  | 4847\ |
## | | [numeric] | Mean : 0.8\ | 1 : 1700 (77.9%) | | (68.96%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 80 | Lagged_Gatherings_Two_Weeks\ | Mean (sd) : 1.7 (1.8)\ | 0 : 918 (47.3%)\ |  | 5089\ |
## | | [numeric] | min < med < max:\ | 1 : 97 ( 5.0%)\ | | (72.4%) |
## | | | 0 < 1 < 4\ | 2 : 166 ( 8.6%)\ | | |
## | | | IQR (CV) : 4 (1) | 3 : 147 ( 7.6%)\ | | |
## | | | | 4 : 612 (31.6%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 81 | Lagged_Gatherings_General_Two_Weeks\ | Min : 0\ | 0 : 68 ( 6.7%)\ |  | 6007\ |
## | | [numeric] | Mean : 0.9\ | 1 : 954 (93.3%) | | (85.46%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 82 | Lagged_Public_Transport_Two_Weeks\ | Mean (sd) : 0.3 (0.6)\ | 0 : 4721 (79.7%)\ |  | 1108\ |
## | | [numeric] | min < med < max:\ | 1 : 645 (10.9%)\ | | (15.76%) |
## | | | 0 < 0 < 2\ | 2 : 555 ( 9.4%) | | |
## | | | IQR (CV) : 0 (2.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 83 | Lagged_Public_Transport_General_Two_Weeks\ | Min : 0\ | 0 : 350 (27.7%)\ |  | 5765\ |
## | | [numeric] | Mean : 0.7\ | 1 : 914 (72.3%) | | (82.02%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 84 | Lagged_Lockdown_Two_Weeks\ | Mean (sd) : 0.9 (1)\ | 0 : 968 (52.7%)\ |  | 5193\ |
## | | [numeric] | min < med < max:\ | 1 : 177 ( 9.6%)\ | | (73.88%) |
## | | | 0 < 0 < 3\ | 2 : 625 (34.0%)\ | | |
## | | | IQR (CV) : 2 (1.1) | 3 : 66 ( 3.6%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 85 | Lagged_Lockdown_General_Two_Weeks\ | Min : 0\ | 0 : 206 (23.7%)\ |  | 6161\ |
## | | [numeric] | Mean : 0.8\ | 1 : 662 (76.3%) | | (87.65%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 86 | Lagged_Internal_Movement_Two_Weeks\ | Mean (sd) : 0.5 (0.8)\ | 0 : 4161 (70.5%)\ |  | 1128\ |
## | | [numeric] | min < med < max:\ | 1 : 358 ( 6.1%)\ | | (16.05%) |
## | | | 0 < 0 < 2\ | 2 : 1382 (23.4%) | | |
## | | | IQR (CV) : 1 (1.6) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 87 | Lagged_Internal_Movement_General_Two_Weeks\ | Min : 0\ | 0 : 547 (31.7%)\ |  | 5305\ |
## | | [numeric] | Mean : 0.7\ | 1 : 1177 (68.3%) | | (75.47%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 88 | Lagged_International_Travel_Two_Weeks\ | Mean (sd) : 1.4 (1.5)\ | 0 : 3072 (51.8%)\ |  | 1100\ |
## | | [numeric] | min < med < max:\ | 1 : 415 ( 7.0%)\ | | (15.65%) |
## | | | 0 < 0 < 4\ | 2 : 304 ( 5.1%)\ | | |
## | | | IQR (CV) : 3 (1.1) | 3 : 1536 (25.9%)\ | | |
## | | | | 4 : 602 (10.2%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 89 | Lagged_Income_Support_Two_Weeks\ | Min : 0\ | 0 : 197 (92.9%)\ |  | 6817\ |
## | | [numeric] | Mean : 0.1\ | 2 : 15 ( 7.1%) | | (96.98%) |
## | | | Max : 2 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 90 | Lagged_Income_Support_General_Two_Weeks\ | 1\. FALSE\ | 13 (86.7%)\ |  | 7014\ |
## | | [logical] | 2\. TRUE | 2 (13.3%) | | (99.79%) |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 91 | Lagged_Debt_Relief_Two_Weeks\ | Mean (sd) : 0.5 (0.8)\ | 0 : 161 (75.9%)\ |  | 6817\ |
## | | [numeric] | min < med < max:\ | 1 : 5 ( 2.4%)\ | | (96.98%) |
## | | | 0 < 0 < 2\ | 2 : 46 (21.7%) | | |
## | | | IQR (CV) : 0 (1.8) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 92 | Lagged_Fiscal_Measures_Two_Weeks\ | Mean (sd) : 1250385439 (31672514058.8)\ | 137 distinct values |  | 1262\ |
## | | [numeric] | min < med < max:\ | | | (17.95%) |
## | | | 0 < 0 < 1957600000000\ | | | |
## | | | IQR (CV) : 0 (25.3) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 93 | Lagged_Campaign_Two_Weeks\ | Mean (sd) : 1.1 (1)\ | 0 : 2542 (42.9%)\ |  | 1105\ |
## | | [numeric] | min < med < max:\ | 1 : 284 ( 4.8%)\ | | (15.72%) |
## | | | 0 < 2 < 2\ | 2 : 3098 (52.3%) | | |
## | | | IQR (CV) : 2 (0.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 94 | Lagged_Campaign_General_Two_Weeks\ | Min : 0\ | 0 : 230 ( 6.8%)\ |  | 3656\ |
## | | [numeric] | Mean : 0.9\ | 1 : 3143 (93.2%) | | (52.01%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 95 | Lagged_Testing_Two_Weeks\ | Mean (sd) : 0.8 (0.8)\ | 0 : 2475 (42.8%)\ |  | 1252\ |
## | | [numeric] | min < med < max:\ | 1 : 2206 (38.2%)\ | | (17.81%) |
## | | | 0 < 1 < 3\ | 2 : 882 (15.3%)\ | | |
## | | | IQR (CV) : 1 (1) | 3 : 214 ( 3.7%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 96 | Lagged_Tracing_Two_Weeks\ | Mean (sd) : 0.7 (0.8)\ | 0 : 2853 (50.7%)\ |  | 1403\ |
## | | [numeric] | min < med < max:\ | 1 : 1376 (24.5%)\ | | (19.96%) |
## | | | 0 < 0 < 2\ | 2 : 1397 (24.8%) | | |
## | | | IQR (CV) : 1 (1.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 97 | Lagged_Health_Care_Two_Weeks\ | Mean (sd) : 448467.4 (13384823.4)\ | 24 distinct values |  | 1578\ |
## | | [numeric] | min < med < max:\ | | | (22.45%) |
## | | | 0 < 0 < 826000000\ | | | |
## | | | IQR (CV) : 0 (29.8) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 98 | Lagged_Stringency_Index_Two_Weeks\ | Mean (sd) : 26.6 (31.3)\ | 289 distinct values |  | 1114\ |
## | | [numeric] | min < med < max:\ | | | (15.85%) |
## | | | 0 < 11.1 < 100\ | | | |
## | | | IQR (CV) : 53.6 (1.2) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 99 | Lagged_Legacy_Stringency_Index_Two_Weeks\ | Mean (sd) : 29.8 (33.9)\ | 173 distinct values |  | 1112\ |
## | | [numeric] | min < med < max:\ | | | (15.82%) |
## | | | 0 < 14.3 < 97.1\ | | | |
## | | | IQR (CV) : 63.3 (1.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 100 | Lagged_School_Closing_Three_Weeks\ | Mean (sd) : 0.9 (1.3)\ | 0 : 3887 (70.1%)\ |  | 1486\ |
## | | [numeric] | min < med < max:\ | 1 : 77 ( 1.4%)\ | | (21.14%) |
## | | | 0 < 0 < 3\ | 2 : 51 ( 0.9%)\ | | |
## | | | IQR (CV) : 3 (1.6) | 3 : 1528 (27.6%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 101 | Lagged_School_Closing_General_Three_Weeks\ | Min : 0\ | 0 : 269 (16.3%)\ |  | 5376\ |
## | | [numeric] | Mean : 0.8\ | 1 : 1384 (83.7%) | | (76.48%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 102 | Lagged_Workplace_Closing_Three_Weeks\ | Mean (sd) : 0.6 (1.1)\ | 0 : 4069 (74.5%)\ |  | 1570\ |
## | | [numeric] | min < med < max:\ | 1 : 395 ( 7.2%)\ | | (22.34%) |
## | | | 0 < 0 < 3\ | 2 : 264 ( 4.8%)\ | | |
## | | | IQR (CV) : 1 (1.9) | 3 : 731 (13.4%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 103 | Lagged_Workplace_Closing_General_Three_Weeks\ | Min : 0\ | 0 : 304 (21.9%)\ |  | 5641\ |
## | | [numeric] | Mean : 0.8\ | 1 : 1084 (78.1%) | | (80.25%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 104 | Lagged_Public_Events_Three_Weeks\ | Mean (sd) : 0.6 (0.9)\ | 0 : 3723 (67.2%)\ |  | 1486\ |
## | | [numeric] | min < med < max:\ | 1 : 252 ( 4.5%)\ | | (21.14%) |
## | | | 0 < 0 < 2\ | 2 : 1568 (28.3%) | | |
## | | | IQR (CV) : 2 (1.5) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 105 | Lagged_Public_Events_General_Three_Weeks\ | Min : 0\ | 0 : 440 (24.4%)\ |  | 5229\ |
## | | [numeric] | Mean : 0.8\ | 1 : 1360 (75.6%) | | (74.39%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 106 | Lagged_Gatherings_Three_Weeks\ | Mean (sd) : 1.5 (1.7)\ | 0 : 911 (53.8%)\ |  | 5337\ |
## | | [numeric] | min < med < max:\ | 1 : 81 ( 4.8%)\ | | (75.93%) |
## | | | 0 < 0 < 4\ | 2 : 138 ( 8.2%)\ | | |
## | | | IQR (CV) : 4 (1.2) | 3 : 119 ( 7.0%)\ | | |
## | | | | 4 : 443 (26.2%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 107 | Lagged_Gatherings_General_Three_Weeks\ | Min : 0\ | 0 : 51 ( 6.5%)\ |  | 6248\ |
## | | [numeric] | Mean : 0.9\ | 1 : 730 (93.5%) | | (88.89%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 108 | Lagged_Public_Transport_Three_Weeks\ | Mean (sd) : 0.2 (0.6)\ | 0 : 4610 (83.2%)\ |  | 1488\ |
## | | [numeric] | min < med < max:\ | 1 : 507 ( 9.2%)\ | | (21.17%) |
## | | | 0 < 0 < 2\ | 2 : 424 ( 7.6%) | | |
## | | | IQR (CV) : 0 (2.4) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 109 | Lagged_Public_Transport_General_Three_Weeks\ | Min : 0\ | 0 : 296 (29.8%)\ |  | 6034\ |
## | | [numeric] | Mean : 0.7\ | 1 : 699 (70.2%) | | (85.84%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 110 | Lagged_Lockdown_Three_Weeks\ | Mean (sd) : 0.7 (1)\ | 0 : 947 (59.6%)\ |  | 5440\ |
## | | [numeric] | min < med < max:\ | 1 : 147 ( 9.2%)\ | | (77.39%) |
## | | | 0 < 0 < 3\ | 2 : 459 (28.9%)\ | | |
## | | | IQR (CV) : 2 (1.3) | 3 : 36 ( 2.3%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 111 | Lagged_Lockdown_General_Three_Weeks\ | Min : 0\ | 0 : 155 (24.1%)\ |  | 6387\ |
## | | [numeric] | Mean : 0.8\ | 1 : 487 (75.9%) | | (90.87%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 112 | Lagged_Internal_Movement_Three_Weeks\ | Mean (sd) : 0.4 (0.8)\ | 0 : 4143 (75.0%)\ |  | 1503\ |
## | | [numeric] | min < med < max:\ | 1 : 324 ( 5.9%)\ | | (21.38%) |
## | | | 0 < 0 < 2\ | 2 : 1059 (19.2%) | | |
## | | | IQR (CV) : 1 (1.8) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 113 | Lagged_Internal_Movement_General_Three_Weeks\ | Min : 0\ | 0 : 473 (34.5%)\ |  | 5659\ |
## | | [numeric] | Mean : 0.7\ | 1 : 897 (65.5%) | | (80.51%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 114 | Lagged_International_Travel_Three_Weeks\ | Mean (sd) : 1.2 (1.5)\ | 0 : 3058 (55.2%)\ |  | 1487\ |
## | | [numeric] | min < med < max:\ | 1 : 415 ( 7.5%)\ | | (21.16%) |
## | | | 0 < 0 < 4\ | 2 : 270 ( 4.9%)\ | | |
## | | | IQR (CV) : 3 (1.2) | 3 : 1345 (24.3%)\ | | |
## | | | | 4 : 454 ( 8.2%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 115 | Lagged_Income_Support_Three_Weeks\ | Min : 0\ | 0 : 190 (96.0%)\ |  | 6831\ |
## | | [numeric] | Mean : 0.1\ | 2 : 8 ( 4.0%) | | (97.18%) |
## | | | Max : 2 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 116 | Lagged_Income_Support_General_Three_Weeks\ | 1\. FALSE\ | 6 (75.0%)\ |  | 7021\ |
## | | [logical] | 2\. TRUE | 2 (25.0%) | | (99.89%) |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 117 | Lagged_Debt_Relief_Three_Weeks\ | Min : 0\ | 0 : 159 (80.3%)\ |  | 6831\ |
## | | [numeric] | Mean : 0.4\ | 2 : 39 (19.7%) | | (97.18%) |
## | | | Max : 2 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 118 | Lagged_Fiscal_Measures_Three_Weeks\ | Mean (sd) : 1325875210.9 | 130 distinct values |  | 1600\ |
## | | [numeric] | (32642217375.7)\ | | | (22.76%) |
## | | | min < med < max:\ | | | |
## | | | 0 < 0 < 1957600000000\ | | | |
## | | | IQR (CV) : 0 (24.6) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 119 | Lagged_Campaign_Three_Weeks\ | Mean (sd) : 1 (1)\ | 0 : 2542 (45.9%)\ |  | 1487\ |
## | | [numeric] | min < med < max:\ | 1 : 277 ( 5.0%)\ | | (21.16%) |
## | | | 0 < 1 < 2\ | 2 : 2723 (49.1%) | | |
## | | | IQR (CV) : 2 (0.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 120 | Lagged_Campaign_General_Three_Weeks\ | Min : 0\ | 0 : 226 ( 7.6%)\ |  | 4038\ |
## | | [numeric] | Mean : 0.9\ | 1 : 2765 (92.4%) | | (57.45%) |
## | | | Max : 1 | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 121 | Lagged_Testing_Three_Weeks\ | Mean (sd) : 0.7 (0.8)\ | 0 : 2475 (45.7%)\ |  | 1615\ |
## | | [numeric] | min < med < max:\ | 1 : 2028 (37.5%)\ | | (22.98%) |
## | | | 0 < 1 < 3\ | 2 : 754 (13.9%)\ | | |
## | | | IQR (CV) : 1 (1.1) | 3 : 157 ( 2.9%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 122 | Lagged_Tracing_Three_Weeks\ | Mean (sd) : 0.7 (0.8)\ | 0 : 2803 (53.2%)\ |  | 1761\ |
## | | [numeric] | min < med < max:\ | 1 : 1219 (23.1%)\ | | (25.05%) |
## | | | 0 < 0 < 2\ | 2 : 1246 (23.6%) | | |
## | | | IQR (CV) : 1 (1.2) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 123 | Lagged_Health_Care_Three_Weeks\ | Mean (sd) : 476715.3 (13799516.8)\ | 24 distinct values |  | 1901\ |
## | | [numeric] | min < med < max:\ | | | (27.05%) |
## | | | 0 < 0 < 826000000\ | | | |
## | | | IQR (CV) : 0 (28.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 124 | Lagged_Stringency_Index_Three_Weeks\ | Mean (sd) : 23.2 (29.2)\ | 273 distinct values |  | 1487\ |
## | | [numeric] | min < med < max:\ | | | (21.16%) |
## | | | 0 < 11.1 < 100\ | | | |
## | | | IQR (CV) : 44.3 (1.3) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 125 | Lagged_Legacy_Stringency_Index_Three_Weeks\ | Mean (sd) : 26.3 (31.9)\ | 170 distinct values |  | 1486\ |
## | | [numeric] | min < med < max:\ | | | (21.14%) |
## | | | 0 < 14.3 < 97.1\ | | | |
## | | | IQR (CV) : 53.3 (1.2) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 126 | Latitude\ | Mean (sd) : 30.8 (27.1)\ | 59 distinct values |  | 20\ |
## | | [numeric] | min < med < max:\ | | | (0.28%) |
## | | | -40.9 < 37.1 < 65\ | | | |
## | | | IQR (CV) : 30.6 (0.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 127 | Longitude\ | Mean (sd) : 26.1 (61.3)\ | 59 distinct values |  | 20\ |
## | | [numeric] | min < med < max:\ | | | (0.28%) |
## | | | -106.3 < 19.5 < 174.9\ | | | |
## | | | IQR (CV) : 49 (2.3) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 128 | prop65\ | Mean (sd) : 13.8 (6.4)\ | 59 distinct values |  | 20\ |
## | | [numeric] | min < med < max:\ | | | (0.28%) |
## | | | 1.1 < 14.8 < 27.6\ | | | |
## | | | IQR (CV) : 11.3 (0.5) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 129 | propurban\ | Mean (sd) : 75.8 (15.8)\ | 58 distinct values |  | 20\ |
## | | [numeric] | min < med < max:\ | | | (0.28%) |
## | | | 34 < 80.2 < 100\ | | | |
## | | | IQR (CV) : 21.1 (0.2) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 130 | popdensity\ | Mean (sd) : 293 (1031.9)\ | 59 distinct values |  | 20\ |
## | | [numeric] | min < med < max:\ | | | (0.28%) |
## | | | 3.2 < 107.9 < 7953\ | | | |
## | | | IQR (CV) : 201.2 (3.5) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 131 | driving\ | Mean (sd) : 82.7 (35.2)\ | 4248 distinct values |  | 1517\ |
## | | [numeric] | min < med < max:\ | | | (21.58%) |
## | | | 8.7 < 94.9 < 186.3\ | | | |
## | | | IQR (CV) : 57.1 (0.4) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 132 | walking\ | Mean (sd) : 82.3 (40.8)\ | 4456 distinct values |  | 1517\ |
## | | [numeric] | min < med < max:\ | | | (21.58%) |
## | | | 5.8 < 91.8 < 362.5\ | | | |
## | | | IQR (CV) : 65.8 (0.5) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 133 | transit\ | Mean (sd) : 72.9 (40.7)\ | 2348 distinct values |  | 4273\ |
## | | [numeric] | min < med < max:\ | | | (60.79%) |
## | | | 7 < 91.1 < 160.6\ | | | |
## | | | IQR (CV) : 78.1 (0.6) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 134 | arrivals\ | Mean (sd) : 19140655.6 (20601992.2)\ | 59 distinct values |  | 0\ |
## | | [numeric] | min < med < max:\ | | | (0%) |
## | | | 1018000 < 11196000 < 89322000\ | | | |
## | | | IQR (CV) : 20126000 (1.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 135 | departures\ | 1\. 10628000\ | 120 ( 2.0%)\ |  | 1080\ |
## | | [numeric]\ | 2\. 108542000\ | 120 ( 2.0%)\ | | (15.36%) |
## | | EAN-8 codes | 3\. 11130000\ | 120 ( 2.0%)\ | | |
## | | | 4\. 11403000\ | 120 ( 2.0%)\ | | |
## | | | 5\. 11883000\ | 120 ( 2.0%)\ | | |
## | | | 6\. 12800000\ | 120 ( 2.0%)\ | | |
## | | | 7\. 13098000\ | 120 ( 2.0%)\ | | |
## | | | 8\. 1446000\ | 120 ( 2.0%)\ | | |
## | | | 9\. 149720000\ | 120 ( 2.0%)\ | | |
## | | | 10\. 1501000\ | 120 ( 2.0%)\ | | |
## | | | [ 40 others ] | 4749 (79.8%) | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 136 | vul_emp\ | Mean (sd) : 17.2 (15.8)\ | 59 distinct values |  | 0\ |
## | | [numeric] | min < med < max:\ | | | (0%) |
## | | | 0.1 < 10.7 < 74.3\ | | | |
## | | | IQR (CV) : 14.4 (0.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 137 | GNI\ | Mean (sd) : 30183.3 (22242.4)\ | 59 distinct values |  | 0\ |
## | | [numeric] | min < med < max:\ | | | (0%) |
## | | | 2020 < 24580 < 84410\ | | | |
## | | | IQR (CV) : 36860 (0.7) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 138 | health_exp\ | Mean (sd) : 2579.5 (2440)\ | 59 distinct values |  | 0\ |
## | | [numeric] | min < med < max:\ | | | (0%) |
## | | | 69.3 < 1529.1 < 10246.1\ | | | |
## | | | IQR (CV) : 3880.5 (0.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 139 | pollution\ | Mean (sd) : 22.4 (21.2)\ | 59 distinct values |  | 0\ |
## | | [numeric] | min < med < max:\ | | | (0%) |
## | | | 5.9 < 16 < 91.2\ | | | |
## | | | IQR (CV) : 14.4 (0.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 140 | EUI_democracy\ | Mean (sd) : 7 (2)\ | 56 distinct values |  | 0\ |
## | | [numeric] | min < med < max:\ | | | (0%) |
## | | | 1.9 < 7.5 < 9.9\ | | | |
## | | | IQR (CV) : 1.8 (0.3) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 141 | freedom_house\ | Mean (sd) : 73.6 (28)\ | 37 distinct values |  | 0\ |
## | | [numeric] | min < med < max:\ | | | (0%) |
## | | | 7 < 88 < 100\ | | | |
## | | | IQR (CV) : 35 (0.4) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 142 | cellular_sub\ | Mean (sd) : 126.7 (22.5)\ | 58 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | 86.9 < 126.2 < 208.5\ | | | |
## | | | IQR (CV) : 19.4 (0.2) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 143 | milex\ | Mean (sd) : 24533672.9 (86338973.5)\ | 56 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | -9 < 4858000 < 655388000\ | | | |
## | | | IQR (CV) : 12165000 (3.5) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 144 | milper\ | Mean (sd) : 216.1 (402)\ | 53 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | 0 < 73 < 2285\ | | | |
## | | | IQR (CV) : 219 (1.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 145 | irst\ | Mean (sd) : 24325.5 (96607.4)\ | 51 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | 0 < 3759 < 731040\ | | | |
## | | | IQR (CV) : 7561 (4) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 146 | pec\ | Mean (sd) : 315019.1 (817525.3)\ | 58 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | 283 < 86845 < 5333707\ | | | |
## | | | IQR (CV) : 206615 (2.6) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 147 | cinc\ | Mean (sd) : 0 (0)\ | 58 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | 0 < 0 < 0.2\ | | | |
## | | | IQR (CV) : 0 (2.5) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 148 | endocrine_deaths\ | Mean (sd) : 5796.8 (9357.1)\ | 13 distinct values |  | 5469\ |
## | | [numeric] | min < med < max:\ | | | (77.81%) |
## | | | 1 < 346 < 27148\ | | | |
## | | | IQR (CV) : 8383 (1.6) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 149 | blood_pressure_deaths\ | Mean (sd) : 6759.3 (12818.6)\ | 46 distinct values |  | 1560\ |
## | | [numeric] | min < med < max:\ | | | (22.19%) |
## | | | 1 < 2310 < 74902\ | | | |
## | | | IQR (CV) : 7395 (1.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 150 | respiratory_deaths\ | Mean (sd) : 68166.6 (113887.7)\ | 47 distinct values |  | 1440\ |
## | | [numeric] | min < med < max:\ | | | (20.49%) |
## | | | 603 < 23856 < 606604\ | | | |
## | | | IQR (CV) : 78734 (1.7) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 151 | Lagged_Mortality_One_Week\ | Mean (sd) : 0 (0)\ | 2338 distinct values |  | 1658\ |
## | | [numeric] | min < med < max:\ | | | (23.59%) |
## | | | 0 < 0 < 0\ | | | |
## | | | IQR (CV) : 0 (4.4) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 152 | Lagged_Mortality_Two_Weeks\ | Mean (sd) : 0 (0)\ | 2020 distinct values |  | 2032\ |
## | | [numeric] | min < med < max:\ | | | (28.91%) |
## | | | 0 < 0 < 0\ | | | |
## | | | IQR (CV) : 0 (5) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 153 | Lagged_Mortality_Three_Weeks\ | Mean (sd) : 0 (0)\ | 1678 distinct values |  | 2439\ |
## | | [numeric] | min < med < max:\ | | | (34.7%) |
## | | | 0 < 0 < 0\ | | | |
## | | | IQR (CV) : 0 (5.6) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 154 | Lagged_Mortality_Four_Weeks\ | Mean (sd) : 0 (0)\ | 1354 distinct values |  | 2848\ |
## | | [numeric] | min < med < max:\ | | | (40.52%) |
## | | | 0 < 0 < 0\ | | | |
## | | | IQR (CV) : 0 (6.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 155 | govt_accountability\ | Mean (sd) : 0.5 (1)\ | 52 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | -1.6 < 0.9 < 1.7\ | | | |
## | | | IQR (CV) : 1.4 (1.8) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 156 | political_stability\ | Mean (sd) : 0.3 (0.7)\ | 52 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | -1.3 < 0.4 < 1.5\ | | | |
## | | | IQR (CV) : 1.2 (2.3) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 157 | govt_effectiveness\ | Mean (sd) : 0.9 (0.7)\ | 54 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | -0.6 < 1.1 < 2.2\ | | | |
## | | | IQR (CV) : 1.2 (0.8) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 158 | regulatory_quality\ | Mean (sd) : 0.9 (0.8)\ | 52 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | -0.9 < 0.9 < 2.1\ | | | |
## | | | IQR (CV) : 1.5 (0.9) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 159 | rule_of_law\ | Mean (sd) : 0.8 (0.8)\ | 52 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | -0.8 < 1 < 2\ | | | |
## | | | IQR (CV) : 1.6 (1.1) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 160 | control_of_corruption\ | Mean (sd) : 0.7 (1)\ | 50 distinct values |  | 120\ |
## | | [numeric] | min < med < max:\ | | | (1.71%) |
## | | | -0.9 < 0.6 < 2.2\ | | | |
## | | | IQR (CV) : 1.8 (1.3) | | | |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
view(dfSummary(Final_Data_Country))
## Switching method to 'browser'
## Output file written: /var/folders/gp/rp10rtns73j6fmfn3240876c0000gn/T//Rtmp8riyG1/filea86965b4252e.html
# For the complete output, please open the summary in the html link.
# Now, I generate a correlation heat map, using a subset of variables from the final dataset.
corrmapvarlist <- c("total_rolling_average_mortality", "Total_Cases_Country", "Total_Deceased_Country", "New_Confirmed_Country", "New_Total_Deceased_Country", "Population", "rolling_average_confirmed", "rolling_average_deceased", "C1_School.closing", "C2_Workplace.closing", "C3_Cancel.public.events", "C4_Restrictions.on.gatherings", "C5_Close.public.transport", "C6_Stay.at.home.requirements", "C7_Restrictions.on.internal.movement", "C8_International.travel.controls", "E1_Income.support", "E2_Debt.contract.relief", "E3_Fiscal.measures", "H1_Public.information.campaigns", "H2_Testing.policy", "H3_Contact.tracing", "H4_Emergency.investment.in.healthcare", "H5_Investment.in.vaccines", "StringencyIndex", "LegacyStringencyIndex", "prop65", "propurban", "driving", "walking")
corrmapvars <- Final_Data_Country[corrmapvarlist]
# Here, I rename the variables to make them easier to read on the map.
corrmapvars <- corrmapvars %>% dplyr::rename(mortality_ra = total_rolling_average_mortality)
corrmapvars <- corrmapvars %>% dplyr::rename(confirmed_ra = rolling_average_confirmed)
corrmapvars <- corrmapvars %>% dplyr::rename(deceased_ra = rolling_average_deceased)
corrmapvars <- corrmapvars %>% dplyr::rename(cases = Total_Cases_Country)
corrmapvars <- corrmapvars %>% dplyr::rename(deaths = Total_Deceased_Country)
corrmapvars <- corrmapvars %>% dplyr::rename(new_cases = New_Confirmed_Country)
corrmapvars <- corrmapvars %>% dplyr::rename(new_deaths = New_Total_Deceased_Country)
corrmapvars <- corrmapvars %>% dplyr::rename(school_closing = C1_School.closing)
corrmapvars <- corrmapvars %>% dplyr::rename(work_closing = C2_Workplace.closing)
corrmapvars <- corrmapvars %>% dplyr::rename(public_event_closing = C3_Cancel.public.events)
corrmapvars <- corrmapvars %>% dplyr::rename(gathering_restrictions = C4_Restrictions.on.gatherings)
corrmapvars <- corrmapvars %>% dplyr::rename(transport_closing = C5_Close.public.transport)
corrmapvars <- corrmapvars %>% dplyr::rename(lockdown = C6_Stay.at.home.requirements)
corrmapvars <- corrmapvars %>% dplyr::rename(internal_restrictions = C7_Restrictions.on.internal.movement)
corrmapvars <- corrmapvars %>% dplyr::rename(travel_restrictions = C8_International.travel.controls)
corrmapvars <- corrmapvars %>% dplyr::rename(income = E1_Income.support)
corrmapvars <- corrmapvars %>% dplyr::rename(debt = E2_Debt.contract.relief)
corrmapvars <- corrmapvars %>% dplyr::rename(fiscal = E3_Fiscal.measures)
corrmapvars <- corrmapvars %>% dplyr::rename(campaigns = H1_Public.information.campaigns)
corrmapvars <- corrmapvars %>% dplyr::rename(testing = H2_Testing.policy)
corrmapvars <- corrmapvars %>% dplyr::rename(tracing = H3_Contact.tracing)
corrmapvars <- corrmapvars %>% dplyr::rename(health_care = H4_Emergency.investment.in.healthcare)
corrmapvars <- corrmapvars %>% dplyr::rename(vaccine = H5_Investment.in.vaccines)
corrmapvars <- corrmapvars %>% dplyr::rename(stringency_index = StringencyIndex)
corrmapvars <- corrmapvars %>% dplyr::rename(legacy_stringency_index = LegacyStringencyIndex)
corrmapvalues <- round(cor(corrmapvars, use = "pairwise.complete.obs"), 2)
# Here, I get the lower triangle of the correlation matrix
get_lower_tri<-function(cormat){
cormat[upper.tri(cormat)] <- NA
return(cormat)
}
# Now, I get the upper triangle of the correlation matrix
get_upper_tri <- function(cormat){
cormat[lower.tri(cormat)]<- NA
return(cormat)
}
reorder_cormat <- function(cormat){
### Use correlation between variables as distance
dd <- as.dist((1-cormat)/2)
hc <- hclust(dd)
cormat <-cormat[hc$order, hc$order]
}
### Reorder the correlation matrix
corrmapvalues <- reorder_cormat(corrmapvalues)
### Get the Upper Triangle
upper_tri <- get_upper_tri(corrmapvalues)
### Melt the Correlation Map
melted_corrmap <- reshape2::melt(upper_tri, na.rm = TRUE)
### Generate the Static Correlation Map
ggheatmap <- ggplot(data = melted_corrmap, aes(x=Var2, y=Var1, fill=value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme(axis.text.x = element_text(angle = 45)) +
theme(axis.text.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0))) +
theme(axis.text.x = element_text(hjust=1))
print(ggheatmap)
### Generate an Interactive Correlation Map (you are able to zoom to specific correlates).
interactive_heatmap <- heatmaply_cor(
cor(corrmapvalues),
row_text_angle = 0,
column_text_angle = 45,
na.value = "grey50",
na.rm = TRUE,
xlab = "Features",
ylab = "Features",
k_col = 0,
k_row = 0,
fontsize_row = 5,
fontsize_col = 5,
plot_method = c("ggplot", "plotly"),
show_dendrogram = c(FALSE, FALSE),
key.title = "Pearson Correlations")
interactive_heatmap
## Following code from: https://www.rstatisticsblog.com/data-science-in-action/lasso-regression/.
str(Final_Data_Country)
## 'data.frame': 7029 obs. of 160 variables:
## $ COUNTRY : chr "Argentina" "Argentina" "Argentina" "Argentina" ...
## $ X1 : num 119 120 112 104 23 22 12 3 1 26 ...
## $ Date : Date, format: "2020-01-01" "2020-01-02" ...
## $ Total_Cases_Country : num NA NA NA NA NA NA NA NA NA NA ...
## $ Total_Deceased_Country : num NA NA NA NA NA NA NA NA NA NA ...
## $ New_Confirmed_Country : num NA NA NA NA NA NA NA NA NA NA ...
## $ New_Total_Deceased_Country : num NA NA NA NA NA NA NA NA NA NA ...
## $ Population : num NA NA NA NA NA NA NA NA NA NA ...
## $ Total_Mortality_Rate_Per_Capita : num NA NA NA NA NA NA NA NA NA NA ...
## $ New_Mortality_Rate_Per_Capita : num NA NA NA NA NA NA NA NA NA NA ...
## $ Total_Cases_Country_Per_Capita : num NA NA NA NA NA NA NA NA NA NA ...
## $ rolling_average_confirmed : num NA NA NA NA NA NA NA NA NA NA ...
## $ rolling_average_deceased : num NA NA NA NA NA NA NA NA NA NA ...
## $ total_rolling_average_mortality : num NA NA NA NA NA NA NA NA NA NA ...
## $ new_rolling_average_mortality : num NA NA NA NA NA NA NA NA NA NA ...
## $ latitude : num NA NA NA NA NA NA NA NA NA NA ...
## $ longitude : num NA NA NA NA NA NA NA NA NA NA ...
## $ C1_School.closing : num 0 0 0 0 0 0 0 0 0 0 ...
## $ C1_Flag : num NA NA NA NA NA NA NA NA NA NA ...
## $ C2_Workplace.closing : num 0 0 0 0 0 0 0 0 0 0 ...
## $ C2_Flag : num NA NA NA NA NA NA NA NA NA NA ...
## $ C3_Cancel.public.events : num 0 0 0 0 0 0 0 0 0 0 ...
## $ C3_Flag : num NA NA NA NA NA NA NA NA NA NA ...
## $ C4_Restrictions.on.gatherings : num NA NA NA NA NA NA NA NA NA NA ...
## $ C4_Flag : num NA NA NA NA NA NA NA NA NA NA ...
## $ C5_Close.public.transport : num 0 0 0 0 0 0 0 0 0 0 ...
## $ C5_Flag : num NA NA NA NA NA NA NA NA NA NA ...
## $ C6_Stay.at.home.requirements : num NA NA NA NA NA NA NA NA NA NA ...
## $ C6_Flag : num NA NA NA NA NA NA NA NA NA NA ...
## $ C7_Restrictions.on.internal.movement : num 0 0 0 0 0 0 0 0 0 0 ...
## $ C7_Flag : num NA NA NA NA NA NA NA NA NA NA ...
## $ C8_International.travel.controls : num 0 0 0 0 0 0 0 0 0 0 ...
## $ E1_Income.support : num NA NA NA NA NA NA NA NA NA NA ...
## $ E1_Flag : logi NA NA NA NA NA NA ...
## $ E2_Debt.contract.relief : num NA NA NA NA NA NA NA NA NA NA ...
## $ E3_Fiscal.measures : num 0 0 0 0 0 0 0 0 0 0 ...
## $ H1_Public.information.campaigns : num 0 0 0 0 0 0 0 0 0 0 ...
## $ H1_Flag : num NA NA NA NA NA NA NA NA NA NA ...
## $ H2_Testing.policy : num 0 0 0 0 0 0 0 0 0 0 ...
## $ H3_Contact.tracing : num 0 0 0 0 0 0 0 0 0 0 ...
## $ H4_Emergency.investment.in.healthcare : num 0 0 0 0 0 0 0 0 0 0 ...
## $ H5_Investment.in.vaccines : num 0 0 0 0 0 0 0 0 0 0 ...
## $ M1_Wildcard : logi NA NA NA NA NA NA ...
## $ StringencyIndex : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LegacyStringencyIndex : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Oxford_Cases : num NA NA NA NA NA NA NA NA NA NA ...
## $ Oxford_Deaths : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_School_Closing_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_School_Closing_General_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Workplace_Closing_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_Workplace_Closing_General_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Public_Events_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_Public_Events_General_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Gatherings_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Gatherings_General_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Public_Transport_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_Public_Transport_General_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Lockdown_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Lockdown_General_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Internal_Movement_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_Internal_Movement_General_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_International_Travel_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_Income_Support_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Income_Support_General_One_Week : logi NA NA NA NA NA NA ...
## $ Lagged_Debt_Relief_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Fiscal_Measures_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_Campaign_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_Campaign_General_One_Week : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Testing_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_Tracing_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_Health_Care_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_Stringency_Index_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_Legacy_Stringency_Index_One_Week : num NA NA NA NA NA NA NA 0 0 0 ...
## $ Lagged_School_Closing_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_School_Closing_General_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Workplace_Closing_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Workplace_Closing_General_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Public_Events_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Public_Events_General_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Gatherings_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Gatherings_General_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Public_Transport_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Public_Transport_General_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Lockdown_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Lockdown_General_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Internal_Movement_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Internal_Movement_General_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_International_Travel_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Income_Support_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Income_Support_General_Two_Weeks : logi NA NA NA NA NA NA ...
## $ Lagged_Debt_Relief_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Fiscal_Measures_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Campaign_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Campaign_General_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Testing_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Tracing_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Health_Care_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Stringency_Index_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## $ Lagged_Legacy_Stringency_Index_Two_Weeks : num NA NA NA NA NA NA NA NA NA NA ...
## [list output truncated]
# The following code gives us every set of possible relationships between our numeric independent variables. Factor variables and lags have been excluded.
# First, government responses linked to the seven-day rolling average of mortality rates per capita.
pairs.panels(Final_Data_Country[c("total_rolling_average_mortality", "StringencyIndex", "E1_Income.support", "E2_Debt.contract.relief", "E3_Fiscal.measures", "H2_Testing.policy", "H3_Contact.tracing", "H4_Emergency.investment.in.healthcare")])
# Next, government responses linked to the total and new mortality rates per capita.
pairs.panels(Final_Data_Country[c("Total_Mortality_Rate_Per_Capita", "StringencyIndex", "E1_Income.support", "E2_Debt.contract.relief", "E3_Fiscal.measures", "H2_Testing.policy", "H3_Contact.tracing", "H4_Emergency.investment.in.healthcare")])
pairs.panels(Final_Data_Country[c("New_Mortality_Rate_Per_Capita", "StringencyIndex", "E1_Income.support", "E2_Debt.contract.relief", "E3_Fiscal.measures", "H2_Testing.policy", "H3_Contact.tracing", "H4_Emergency.investment.in.healthcare")])
library(lubridate)
library(zoo)
library(quantmod)
library(fBasics)
library(tseries)
library(sandwich)
library(lmtest)
library(lattice)
library(xtable)
##
## Attaching package: 'xtable'
## The following objects are masked from 'package:Hmisc':
##
## label, label<-
## The following object is masked from 'package:timeSeries':
##
## align
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##
## align
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##
## label, label<-
library(vars)
library(plyr)
## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
##
## Attaching package: 'plyr'
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## is.discrete, summarize
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## arrange, mutate, rename, summarise
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## rename, round_any
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
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## compact
library(gridExtra)
library(corrplot)
library(ggplot2)
library(reshape2)
## Warning: package 'reshape2' was built under R version 3.6.2
##
## Attaching package: 'reshape2'
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## dcast, melt
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## colsplit, melt, recast
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## smiths
library(data.table)
library(rvest)
## Loading required package: xml2
## Warning: package 'xml2' was built under R version 3.6.2
##
## Attaching package: 'xml2'
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## as_list
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## html
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## pluck
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## guess_encoding
library(plm)
library(tidyverse)
library(stringr)
library(stargazer)
library(haven)
dat<-Final_Data_Country
# Remove factor variables for government responses and lagged variables
dat<-dat %>% dplyr::select(-contains("Flag"))
dat<-dat %>% dplyr::select(-contains("Lagged"))
dat$latitude <- NULL
dat$longitude <- NULL
gps.country <- read_dta("data/global_preference_survey_country.dta")
# Change country names for merge
gps.country[which(gps.country$country == "South Korea"),"country"] <- "Korea, South"
gps.country[which(gps.country$country == "Czech Republic"),"country"] <- "Czechia"
gps.country[which(gps.country$country == "United States"),"country"] <- "US"
gps.individual <- read_dta("data/global_preference_survey_individual.dta")
# Change country names for merge
gps.individual[which(gps.individual$country == "South Korea"),"country"] <- "Korea, South"
gps.individual[which(gps.individual$country == "Czech Republic"),"country"] <- "Czechia"
gps.individual[which(gps.individual$country == "United States"),"country"] <- "US"
gps.individual$age <- as.character(gps.individual$age)
gps.individual[which(gps.individual$age == "99 99+"), "age"] <- "99"
gps.individual[which(gps.individual$age == "100 (Refused"), "age"] <- NA
gps.individual$age <- as.numeric(gps.individual$age)
gps.individual.pop65 <- gps.individual[which(!is.na(gps.individual$age) & gps.individual$age > 65), ]
gps.individual.pop65 <- gps.individual.pop65[which(!is.na(gps.individual.pop65$trust)),]
gps.country.pop65 <- ddply(gps.individual.pop65, "country", function(X) data.frame(wm.trust = weighted.mean(X$trust, X$wgt)))
colnames(gps.country)[1] <- "COUNTRY"
dat <- merge(dat, gps.country, by=c("COUNTRY"), all.x=TRUE)
colnames(gps.country.pop65)[1] <- "COUNTRY"
dat <- merge(dat, gps.country.pop65, by=c("COUNTRY"), all.x=TRUE)
p.dat_cum<-pdata.frame(dat, index = c("COUNTRY", "Date"))
p.dat_cum$Date<-as.Date(p.dat_cum$Date,"%Y-%m-%d")
#reorder
p.dat_cum<-p.dat_cum[order(p.dat_cum$Date),]
p.dat_cum<-p.dat_cum[order(p.dat_cum$COUNTRY),]
colnames(p.dat_cum)[colnames(p.dat_cum) == "Total_Cases_Country"] <- "Total_Case_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "Total_Deceased_Country"] <- "Total_Death_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "New_Confirmed_Country"] <- "New_Case_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "New_Total_Deceased_Country"] <- "New_Death_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "Total_Mortality_Rate_Per_Capita"] <- "Total_Mortality_Rate"
colnames(p.dat_cum)[colnames(p.dat_cum) == "New_Mortality_Rate_Per_Capita"] <- "New_Mortality_Rate"
colnames(p.dat_cum)[colnames(p.dat_cum) == "Total_Cases_Country_Per_Capita"] <- "Total_Case_Rate"
colnames(p.dat_cum)[colnames(p.dat_cum) == "rolling_average_confirmed"] <- "RollingAverage_New_Case_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "rolling_average_deceased"] <- "RollingAverage_New_Death_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "total_rolling_average_mortality"] <- "RollingAverage_Total_Mortality_Rate"
colnames(p.dat_cum)[colnames(p.dat_cum) == "new_rolling_average_mortality"] <- "RollingAverage_New_Mortality_Rate"
colnames(p.dat_cum)[colnames(p.dat_cum) == "EUI_democracy"] <- "EIU_Democracy"
p.dat_cum$RollingAverage_Total_Mortality_Rate <- NULL
p.dat_cum$longitude <- NULL
p.dat_cum$New_Case_Rate = p.dat_cum$New_Case_Country/p.dat_cum$Population
p.dat_cum <- p.dat_cum %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(RollingAverage_New_Case_Rate = frollmean(New_Case_Rate, 7,na.rm=TRUE))
p.dat_cum <- p.dat_cum %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(Total_Mortality_Rate_Growth = log(Total_Mortality_Rate) - Lag(log(Total_Mortality_Rate),7))
p.dat_cum <- p.dat_cum %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(Total_Case_Rate_Growth = log(Total_Case_Rate) - Lag(log(Total_Case_Rate),7))
p.dat_cum <- p.dat_cum %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(New_Case_Rate_Growth = log(RollingAverage_New_Case_Rate) - Lag(log(RollingAverage_New_Case_Rate),7))
p.dat_cum <- p.dat_cum %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(New_Mortality_Rate_Growth = log(RollingAverage_New_Mortality_Rate) - Lag(log(RollingAverage_New_Mortality_Rate),7))
p.dat_cum$respiratory_deaths_rate <- 1/3*(p.dat_cum$respiratory_deaths/p.dat_cum$Population)
p.dat_cum <- p.dat_cum %>%
dplyr::select(c("COUNTRY", "X1", "Date",
"Total_Case_Country", "Total_Death_Country", "New_Case_Country", "New_Death_Country", "Population",
"Total_Case_Rate", "Total_Mortality_Rate", "New_Case_Rate", "New_Mortality_Rate",
"RollingAverage_New_Case_Country", "RollingAverage_New_Death_Country",
"RollingAverage_New_Case_Rate", "RollingAverage_New_Mortality_Rate",
"Total_Case_Rate_Growth", "Total_Mortality_Rate_Growth",
"New_Case_Rate_Growth", "New_Mortality_Rate_Growth"), everything())
p.dat_cum$Total_Case_Rate_Growth[is.nan(p.dat_cum$Total_Case_Rate_Growth)] <- NA
p.dat_cum$Total_Mortality_Rate_Growth[is.nan(p.dat_cum$Total_Mortality_Rate_Growth)] <- NA
p.dat_cum$New_Case_Rate_Growth[is.nan(p.dat_cum$New_Case_Rate_Growth)] <- NA
p.dat_cum$New_Mortality_Rate_Growth[is.nan(p.dat_cum$New_Mortality_Rate_Growth)] <- NA
p.dat_cum$New_Mortality_Rate_Growth[is.infinite(-p.dat_cum$New_Mortality_Rate_Growth)] <- Inf
colnames(p.dat_cum)
## [1] "COUNTRY"
## [2] "X1"
## [3] "Date"
## [4] "Total_Case_Country"
## [5] "Total_Death_Country"
## [6] "New_Case_Country"
## [7] "New_Death_Country"
## [8] "Population"
## [9] "Total_Case_Rate"
## [10] "Total_Mortality_Rate"
## [11] "New_Case_Rate"
## [12] "New_Mortality_Rate"
## [13] "RollingAverage_New_Case_Country"
## [14] "RollingAverage_New_Death_Country"
## [15] "RollingAverage_New_Case_Rate"
## [16] "RollingAverage_New_Mortality_Rate"
## [17] "Total_Case_Rate_Growth"
## [18] "Total_Mortality_Rate_Growth"
## [19] "New_Case_Rate_Growth"
## [20] "New_Mortality_Rate_Growth"
## [21] "C1_School.closing"
## [22] "C2_Workplace.closing"
## [23] "C3_Cancel.public.events"
## [24] "C4_Restrictions.on.gatherings"
## [25] "C5_Close.public.transport"
## [26] "C6_Stay.at.home.requirements"
## [27] "C7_Restrictions.on.internal.movement"
## [28] "C8_International.travel.controls"
## [29] "E1_Income.support"
## [30] "E2_Debt.contract.relief"
## [31] "E3_Fiscal.measures"
## [32] "H1_Public.information.campaigns"
## [33] "H2_Testing.policy"
## [34] "H3_Contact.tracing"
## [35] "H4_Emergency.investment.in.healthcare"
## [36] "H5_Investment.in.vaccines"
## [37] "M1_Wildcard"
## [38] "StringencyIndex"
## [39] "LegacyStringencyIndex"
## [40] "Oxford_Cases"
## [41] "Oxford_Deaths"
## [42] "Latitude"
## [43] "Longitude"
## [44] "prop65"
## [45] "propurban"
## [46] "popdensity"
## [47] "driving"
## [48] "walking"
## [49] "transit"
## [50] "arrivals"
## [51] "departures"
## [52] "vul_emp"
## [53] "GNI"
## [54] "health_exp"
## [55] "pollution"
## [56] "EIU_Democracy"
## [57] "freedom_house"
## [58] "cellular_sub"
## [59] "milex"
## [60] "milper"
## [61] "irst"
## [62] "pec"
## [63] "cinc"
## [64] "endocrine_deaths"
## [65] "blood_pressure_deaths"
## [66] "respiratory_deaths"
## [67] "govt_accountability"
## [68] "political_stability"
## [69] "govt_effectiveness"
## [70] "regulatory_quality"
## [71] "rule_of_law"
## [72] "control_of_corruption"
## [73] "isocode"
## [74] "patience"
## [75] "risktaking"
## [76] "posrecip"
## [77] "negrecip"
## [78] "altruism"
## [79] "trust"
## [80] "wm.trust"
## [81] "respiratory_deaths_rate"
p.dat_cum <- p.dat_cum %>% ungroup()
p.dat_cum <- pdata.frame(p.dat_cum, index = c("COUNTRY", "Date"))
p.dat_cum <- p.dat_cum[order(p.dat_cum$Date),]
p.dat_cum <- p.dat_cum[order(p.dat_cum$COUNTRY),]
###baseline projection regressions, remove 'infinite' values. two-way FE, HAC robust SEs, clustered on country
regmoda<-plm(Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))
regmodb<-plm(Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))
regmodc<-plm(Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))
regmodd<-plm(Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))
stargazer(digits=6,regmoda,regmodb,regmodc,regmodd,type="html",se=list(se.a[,2],se.b[,2],se.c[,2],se.d[,2]),out=("Table_Appendix_baseline_panel_output.htm"),
dep.var.labels=c("Cumulative Mortality Growth (t)"),
covariate.labels=c("Cum. Mortality Growth (t-7)","Stringency (t-14)","Cum. Mortality Growth (t-14)","Stringency (t-21)","Cum. Mortality Growth (t-21)","Stringency (t-28)","Cum. Mortality Growth (t-28)","Stringency (t-35)"), df = FALSE,omit.stat="adj.rsq",notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","HAC robust standard errors, clustered by country. Time and Country FEs."),notes.append=F,notes.align ="l",title="Local Projection Regressions (Appendix)",add.lines = list(c("Fixed effects?", "Y", "Y","Y","Y")))
### plot of 10-unit increase in Stringency effects over time on future mortality growth
p.dat<-c(coef(regmoda)[2],coef(regmodb)[2],coef(regmodc)[2],coef(regmodd)[2])
p.dat<-cbind(p.dat,p.dat+1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
p.dat<-cbind(p.dat,p.dat[,1]-1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
colnames(p.dat)<-c("mean","upper","lower")
p.dat<-data.frame(p.dat)*10*100
Plot_32 <-ggplot(data=p.dat,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean))+geom_point(size=4,shape=21,fill="grey")+geom_hline(yintercept=0,linetype=2)+xlab("Increase in Stringency Index of 10 units")+theme_bw()+ylab("Effect on Cum. Mortality Growth (%)")+geom_errorbar(aes(ymin=lower,ymax=upper))
Plot_32
Plot_33 <-ggplot(data=p.dat,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean))+geom_point(size=4,shape=21,fill="grey")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cum. Mortality Growth (%)")+geom_errorbar(aes(ymin=lower,ymax=upper))+ggtitle("Average Impact")+ylim(-15,5)
Plot_33
###baseline projection regressions, remove 'infinite' values. two-way FE, HAC robust SEs, clustered on country
var.list <- c("prop65", "propurban", "Latitude", "Longitude", "popdensity", "arrivals", "departures", "vul_emp", "GNI", "health_exp", "pollution", "EIU_Democracy", "trust", "risktaking")
label.list <- c("Proportion 65+", "Proportion Urban", "Latitude", "Longitude", "Population Density", "Log(Arrivals)", "Log(Departures)", "Vulnerable Employees", "Log(GNI per capita)", "Health Expenditures", "Pollution", "EIU Democracy", "Trust", "Risk Taking")
p.dat.list <- list()
s.list <- list()
for (i in c(1:length(var.list))){
var <- var.list[i]
label <- label.list[i]
if (var == "arrivals" || var == "departures" || var == "GNI"){
regmoda<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14))+Lag(StringencyIndex,c(14)):","log(",var,")")),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))
regmodb<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21))+Lag(StringencyIndex,c(21)):","log(",var,")")),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))
regmodc<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28))+Lag(StringencyIndex,c(28)):","log(",var,")")),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))
regmodd<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35))+Lag(StringencyIndex,c(35)):","log(",var,")")),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))
}else{
regmoda<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14))+Lag(StringencyIndex,c(14)):",var)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))
regmodb<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21))+Lag(StringencyIndex,c(21)):",var)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))
regmodc<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28))+Lag(StringencyIndex,c(28)):",var)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))
regmodd<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35))+Lag(StringencyIndex,c(35)):",var)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))
}
stargazer(digits=6,regmoda,regmodb,regmodc,regmodd,type="html",se=list(se.a[,2],se.b[,2],se.c[,2],se.d[,2]),out=paste("Table_Appendix_panel_",var,"_weekly_reg_output.htm"),
dep.var.labels=c("Cumulative Mortality Growth (t)"),
dep.var.labels.include = FALSE,
covariate.labels=c("Cum. Mortality Growth (t-7)","Stringency (t-14)",paste("Stringency (t-14) X ", label),"Cum. Mortality Growth (t-14)","Stringency (t-21)",paste("Stringency (t-21) X ", label),"Cum. Mortality Growth (t-21)","Stringency (t-28)",paste("Stringency (t-28) X ", label),"Cum. Mortality Growth (t-28)","Stringency (t-35)",paste("Stringency (t-35) X ", label)), df = FALSE,omit.stat="adj.rsq",notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","HAC robust standard errors, clustered by country. Time and Country FEs."),notes.append=F,notes.align ="l",title=paste("Heterogeneity: ", label),add.lines = list(c("Fixed effects?", "Y", "Y","Y","Y")))
### plot of 10-unit increase in Stringency effects over time on future mortality growth
p.dat<-c(coef(regmoda)[2],coef(regmodb)[2],coef(regmodc)[2],coef(regmodd)[2])
i.dat<-c(coef(regmoda)[3],coef(regmodb)[3],coef(regmodc)[3],coef(regmodd)[3])
effect.dat<-cbind(p.dat, i.dat)
#p.dat<-cbind(p.dat,p.dat+1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
#p.dat<-cbind(p.dat,p.dat[,1]-1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
colnames(effect.dat)<-c("mean.1", "mean.2")
p.dat.list[[i]]<-data.frame(effect.dat)*10*100
###figure out the 25% percentile and 75% percentile of elderly population rate across countries, to compare effects of a 10-unit rise in stringency level
if (var == "arrivals" || var == "departures" || var == "GNI"){
s.list[[i]] <- summary(unique(log(p.dat_cum[[var]])))
}else{
s.list[[i]] <- summary(unique(p.dat_cum[[var]]))
}
s.list
}
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Proportion 65+</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.075232</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.052933)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.002952</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.002433)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Proportion 65+</td><td>-0.000741<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000147)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>-0.002603</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.034343)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.000323</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.001853)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Proportion 65+</td><td></td><td>-0.000476<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000106)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.059215<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026212)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.002296</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001457)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Proportion 65+</td><td></td><td></td><td>-0.000262<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000120)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.084322<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.035553)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.003998<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.002198)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Proportion 65+</td><td></td><td></td><td></td><td>-0.000029</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000148)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.183163</td><td>0.197537</td><td>0.178810</td><td>0.124033</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>126.916600<sup>***</sup></td><td>109.542500<sup>***</sup></td><td>70.258980<sup>***</sup></td><td>29.498940<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Proportion Urban</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.103173<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.056754)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.003729</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.004706)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Proportion Urban</td><td>-0.000150<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000059)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.014070</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.037639)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.000371</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003759)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Proportion Urban</td><td></td><td>-0.000100<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000048)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.047184<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026737)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.003409</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.002788)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Proportion Urban</td><td></td><td></td><td>-0.000035</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000041)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.083614<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.033956)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.003924</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003092)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Proportion Urban</td><td></td><td></td><td></td><td>-0.000007</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000047)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.130300</td><td>0.155460</td><td>0.155396</td><td>0.123904</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>84.798780<sup>***</sup></td><td>81.914130<sup>***</sup></td><td>59.366550<sup>***</sup></td><td>29.464150<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Latitude</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.091215</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.057141)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.001517</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.002388)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Latitude</td><td>-0.000150<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000040)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.015918</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.036961)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.004441<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002217)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Latitude</td><td></td><td>-0.000062<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000032)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.050375<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.027041)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.004255<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001608)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Latitude</td><td></td><td></td><td>-0.000045<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000020)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.085421<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.033133)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.003774<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001434)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Latitude</td><td></td><td></td><td></td><td>-0.000018</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000030)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.154164</td><td>0.149144</td><td>0.163187</td><td>0.125262</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>103.160300<sup>***</sup></td><td>78.002870<sup>***</sup></td><td>62.923420<sup>***</sup></td><td>29.833310<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Longitude</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.107097<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.056050)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.006999<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.002008)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Longitude</td><td>0.000044<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000019)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.015748</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.038032)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.006766<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002038)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Longitude</td><td></td><td>0.000037<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000018)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.045166<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026575)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.005919<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001736)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Longitude</td><td></td><td></td><td>0.000011</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000015)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.086320<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.032725)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.004601<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001479)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Longitude</td><td></td><td></td><td></td><td>0.000015</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000020)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.131542</td><td>0.170127</td><td>0.155553</td><td>0.130370</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>85.729790<sup>***</sup></td><td>91.226900<sup>***</sup></td><td>59.437240<sup>***</sup></td><td>31.232150<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Population Density</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.126328<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.058592)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.006724<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001857)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Population Density</td><td>0.0000004</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000001)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.030691</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.036516)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.007053<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002024)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Population Density</td><td></td><td>0.000003</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000004)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.041399</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026243)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006096<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001894)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Population Density</td><td></td><td></td><td>0.000001</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000003)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.080699<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.028253)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.005161<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001833)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Population Density</td><td></td><td></td><td></td><td>0.000004</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000005)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.104353</td><td>0.135275</td><td>0.151898</td><td>0.126755</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>65.945510<sup>***</sup></td><td>69.614570<sup>***</sup></td><td>57.790700<sup>***</sup></td><td>30.240540<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(Arrivals)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.104480<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.055939)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.044519<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.014335)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(Arrivals)</td><td>-0.003076<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000855)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.011777</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.032059)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.044774<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.011129)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(Arrivals)</td><td></td><td>-0.003087<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000691)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.052692<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.022080)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.032350<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.011842)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(Arrivals)</td><td></td><td></td><td>-0.002296<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000759)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.092806<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.030015)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.029739<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.012662)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(Arrivals)</td><td></td><td></td><td></td><td>-0.002048<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000803)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.143237</td><td>0.210514</td><td>0.210993</td><td>0.168633</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>94.626010<sup>***</sup></td><td>118.657500<sup>***</sup></td><td>86.286100<sup>***</sup></td><td>42.257950<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(Departures)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.119221<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.056501)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.034250<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.016669)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(Departures)</td><td>-0.002578<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000990)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.012672</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.030975)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.026553<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.014353)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(Departures)</td><td></td><td>-0.002158<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000895)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.081513<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.030486)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.028357<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.010418)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(Departures)</td><td></td><td></td><td>-0.002189<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000678)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.092880<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.044006)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.036899<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.010839)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(Departures)</td><td></td><td></td><td></td><td>-0.002535<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000719)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,604</td><td>1,291</td><td>968</td><td>650</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.194975</td><td>0.315715</td><td>0.369098</td><td>0.245081</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>118.838300<sup>***</sup></td><td>179.322800<sup>***</sup></td><td>165.759000<sup>***</sup></td><td>58.652730<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Vulnerable Employees</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.105264<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.056265)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.009835<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001965)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Vulnerable Employees</td><td>0.000158<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000071)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.022316</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.037793)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.007647<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002168)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Vulnerable Employees</td><td></td><td>0.000053</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000052)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.044589<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026810)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006279<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.002116)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Vulnerable Employees</td><td></td><td></td><td>0.000020</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000046)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.085713<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.033703)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.004941<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001942)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Vulnerable Employees</td><td></td><td></td><td></td><td>0.000028</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000053)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.129922</td><td>0.138502</td><td>0.152607</td><td>0.125520</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>84.516550<sup>***</sup></td><td>71.542310<sup>***</sup></td><td>58.108830<sup>***</sup></td><td>29.903570<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(GNI per capita)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.097395<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.054196)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.022969<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.008963)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(GNI per capita)</td><td>-0.003051<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000895)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.011759</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.036610)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.011792<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.007114)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(GNI per capita)</td><td></td><td>-0.001890<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000737)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.049609<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026893)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.002661</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.005209)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(GNI per capita)</td><td></td><td></td><td>-0.000874</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000591)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.084918<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.033721)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.001720</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.006171)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(GNI per capita)</td><td></td><td></td><td></td><td>-0.000273</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000668)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.153794</td><td>0.170634</td><td>0.162819</td><td>0.124692</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>102.867700<sup>***</sup></td><td>91.554300<sup>***</sup></td><td>62.753710<sup>***</sup></td><td>29.677990<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Health Expenditures</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.108591<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.058168)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.003926<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001869)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Health Expenditures</td><td>-0.000001<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.0000003)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.017615</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.039096)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.004372<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.001754)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Health Expenditures</td><td></td><td>-0.000001<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.0000003)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.047640<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.027409)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.004588<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001495)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Health Expenditures</td><td></td><td></td><td>-0.0000005</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.0000003)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.084502<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.031001)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.003898<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001282)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Health Expenditures</td><td></td><td></td><td></td><td>-0.0000002</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.0000002)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.143209</td><td>0.180522</td><td>0.171206</td><td>0.126027</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>94.604600<sup>***</sup></td><td>98.028330<sup>***</sup></td><td>66.653960<sup>***</sup></td><td>30.041820<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Pollution</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.121538<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.058210)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.008450<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001891)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Pollution</td><td>0.000069<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000019)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.025266</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.037170)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.008395<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002009)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Pollution</td><td></td><td>0.000070<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000030)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.043744</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.027262)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006751<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001949)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Pollution</td><td></td><td></td><td>0.000034</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000023)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.083370<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.030041)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.005010<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001621)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Pollution</td><td></td><td></td><td></td><td>0.000026</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000017)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.114877</td><td>0.154403</td><td>0.158611</td><td>0.126791</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>73.458810<sup>***</sup></td><td>81.255530<sup>***</sup></td><td>60.826100<sup>***</sup></td><td>30.250230<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: EIU Democracy</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.118904<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.057705)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.000664</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.003637)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X EIU Democracy</td><td>-0.001073<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000443)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.022984</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.037570)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.001000</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002591)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X EIU Democracy</td><td></td><td>-0.001099<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000345)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.045614<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.027451)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.002008</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001914)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X EIU Democracy</td><td></td><td></td><td>-0.000551<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000300)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.083701<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.031253)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.003151</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.002307)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X EIU Democracy</td><td></td><td></td><td></td><td>-0.000174</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000354)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.119085</td><td>0.163146</td><td>0.161532</td><td>0.124466</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>76.513970<sup>***</sup></td><td>86.753590<sup>***</sup></td><td>62.162280<sup>***</sup></td><td>29.616560<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Trust</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.212853<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.053297)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.007698<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001974)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Trust</td><td>0.009153<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.003998)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.047107</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.044812)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.007957<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002014)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Trust</td><td></td><td>0.006320<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003475)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.045732</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.030809)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006647<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001880)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Trust</td><td></td><td></td><td>0.000370</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003403)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.096406<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.032801)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.004803<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001715)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Trust</td><td></td><td></td><td></td><td>-0.002180</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.004019)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,482</td><td>1,208</td><td>924</td><td>643</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.194906</td><td>0.179490</td><td>0.171133</td><td>0.145638</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>109.425000<sup>***</sup></td><td>79.407650<sup>***</sup></td><td>55.883380<sup>***</sup></td><td>30.740290<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Risk Taking</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.213216<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.053427)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.006621<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001802)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Risk Taking</td><td>0.001681</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.007420)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.053208</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.044486)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.007507<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002027)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Risk Taking</td><td></td><td>-0.003379</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.005674)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.043984</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.032246)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006671<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001800)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Risk Taking</td><td></td><td></td><td>-0.001016</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003943)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.087099<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.034356)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.005275<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001531)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Risk Taking</td><td></td><td></td><td></td><td>-0.005940</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.004463)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,482</td><td>1,208</td><td>924</td><td>643</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.168322</td><td>0.159572</td><td>0.171362</td><td>0.152460</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>91.479720<sup>***</sup></td><td>68.922730<sup>***</sup></td><td>55.973940<sup>***</sup></td><td>32.439340<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
#plot
Plot_34 <- ggplot(data=p.dat.list[[1]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*19.400))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*19.400),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+geom_point(size=4,aes(y=mean.1+mean.2*8.283),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*8.283)),linetype=2)+ggtitle(label.list[1])+ylim(-20,10)
Plot_34
#plot
Plot_35 <- ggplot(data=p.dat.list[[2]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*86.56))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*86.56),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*66.47),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*66.47)),linetype=2)+ggtitle(label.list[2])+ylim(-20,10)
Plot_35
#plot
Plot_36 <- ggplot(data=p.dat.list[[3]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*50.83))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*50.83),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*22.01),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*22.01)),linetype=2)+ggtitle(label.list[3])+ylim(-20,10)
Plot_36
#plot
Plot_37 <- ggplot(data=p.dat.list[[4]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*49.333))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*49.333),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*3.342),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*3.342)),linetype=2)+ggtitle(label.list[4])+ylim(-20,10)
Plot_37
#plot
Plot_38 <- ggplot(data=p.dat.list[[5]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*223.847))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*223.847),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*33.375),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*33.375)),linetype=2)+ggtitle(label.list[5])+ylim(-20,10)
Plot_38
#plot
Plot_39 <- ggplot(data=p.dat.list[[6]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.94))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.94),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*15.30),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*15.30)),linetype=2)+ggtitle(label.list[6])+ylim(-20,10)
Plot_39
#plot
Plot_40 <- ggplot(data=p.dat.list[[7]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.79))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.79),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*15.02),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*15.02)),linetype=2)+ggtitle(label.list[7])+ylim(-20,10)
Plot_40
#plot
Plot_41 <- ggplot(data=p.dat.list[[8]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*21.797))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*21.797),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*7.407),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*7.407)),linetype=2)+ggtitle(label.list[8])+ylim(-20,10)
Plot_41
Plot_42 <- ggplot(data=p.dat.list[[9]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*10.747))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*10.747),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*9.242),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*9.242)),linetype=2)+ggtitle(label.list[9])+ylim(-20,10)
Plot_42
Plot_43 <- ggplot(data=p.dat.list[[10]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*4292.73))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*4292.73),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*542.55),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*542.55)),linetype=2)+ggtitle(label.list[10])+ylim(-20,10)
Plot_43
Plot_44 <- ggplot(data=p.dat.list[[11]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*23.084))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*23.084),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*10.419),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*10.419)),linetype=2)+ggtitle(label.list[11])+ylim(-20,10)
Plot_44
Plot_45 <- ggplot(data=p.dat.list[[12]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*8.207))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*8.207),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*6.570),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*6.570)),linetype=2)+ggtitle(label.list[12])+ylim(-20,10)
Plot_45
Plot_46 <- ggplot(data=p.dat.list[[13]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.28083))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.28083),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*-0.13294),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*-0.13294)),linetype=2)+ggtitle(label.list[13])+ylim(-20,10)
Plot_46
Plot_47 <- ggplot(data=p.dat.list[[14]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.06918))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.06918),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*-0.15774),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*-0.15774)),linetype=2)+ggtitle(label.list[14])+ylim(-20,10)
Plot_47
Plot_48 <- grid.arrange(Plot_34, Plot_35, Plot_36, Plot_37, Plot_38, Plot_39, Plot_40, Plot_41, Plot_42, Plot_43, Plot_44, Plot_45, ncol=4,nrow=3)
Plot_48
## TableGrob (3 x 4) "arrange": 12 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (1-1,4-4) arrange gtable[layout]
## 5 5 (2-2,1-1) arrange gtable[layout]
## 6 6 (2-2,2-2) arrange gtable[layout]
## 7 7 (2-2,3-3) arrange gtable[layout]
## 8 8 (2-2,4-4) arrange gtable[layout]
## 9 9 (3-3,1-1) arrange gtable[layout]
## 10 10 (3-3,2-2) arrange gtable[layout]
## 11 11 (3-3,3-3) arrange gtable[layout]
## 12 12 (3-3,4-4) arrange gtable[layout]
library(lubridate)
library(zoo)
library(quantmod)
library(fBasics)
library(tseries)
library(sandwich)
library(lmtest)
library(lattice)
library(xtable)
library(vars)
library(plyr)
library(gridExtra)
library(corrplot)
library(ggplot2)
library(reshape2)
library(data.table)
library(rvest)
library(plm)
library(stringr)
library(stargazer)
library(survival)
library(ggfortify)
## Warning: package 'ggfortify' was built under R version 3.6.2
library(haven)
# Remove factor variables for government responses
dat_cs <-Final_Data_Country[,-c(48:125,151:154)]
gps.country <- read_dta("data/global_preference_survey_country.dta")
# Change country names for merge
gps.country[which(gps.country$country == "South Korea"),"country"] <- "Korea, South"
gps.country[which(gps.country$country == "Czech Republic"),"country"] <- "Czechia"
gps.country[which(gps.country$country == "United States"),"country"] <- "US"
gps.individual <- read_dta("data/global_preference_survey_individual.dta")
# Change country names for merge
gps.individual[which(gps.individual$country == "South Korea"),"country"] <- "Korea, South"
gps.individual[which(gps.individual$country == "Czech Republic"),"country"] <- "Czechia"
gps.individual[which(gps.individual$country == "United States"),"country"] <- "US"
gps.individual$age <- as.character(gps.individual$age)
gps.individual[which(gps.individual$age == "99 99+"), "age"] <- "99"
gps.individual[which(gps.individual$age == "100 (Refused"), "age"] <- NA
gps.individual$age <- as.numeric(gps.individual$age)
gps.individual.pop65 <- gps.individual[which(!is.na(gps.individual$age) & gps.individual$age > 65), ]
gps.individual.pop65 <- gps.individual.pop65[which(!is.na(gps.individual.pop65$trust)),]
gps.country.pop65 <- ddply(gps.individual.pop65, "country", function(X) data.frame(wm.trust = weighted.mean(X$trust, X$wgt)))
colnames(gps.country)[1] <- "COUNTRY"
dat_cs <- merge(dat_cs, gps.country, by=c("COUNTRY"), all.x=TRUE)
colnames(gps.country.pop65)[1] <- "COUNTRY"
dat_cs <- merge(dat_cs, gps.country.pop65, by=c("COUNTRY"), all.x=TRUE)
cs_cumulative<-pdata.frame(dat_cs, index = c("COUNTRY", "Date"))
cs_cumulative$Date<-as.Date(cs_cumulative$Date,"%Y-%m-%d")
#reorder
cs_cumulative<-cs_cumulative[order(cs_cumulative$Date),]
cs_cumulative<-cs_cumulative[order(cs_cumulative$COUNTRY),]
colnames(cs_cumulative)[4] <- "Total_Case_Country"
colnames(cs_cumulative)[5] <- "Total_Death_Country"
colnames(cs_cumulative)[6] <- "New_Case_Country"
colnames(cs_cumulative)[7] <- "New_Death_Country"
colnames(cs_cumulative)[9] <- "Total_Mortality_Rate"
colnames(cs_cumulative)[10] <- "New_Mortality_Rate"
colnames(cs_cumulative)[11] <- "Total_Case_Rate"
colnames(cs_cumulative)[12] <- "RollingAverage_New_Case_Country"
colnames(cs_cumulative)[13] <- "RollingAverage_New_Death_Country"
colnames(cs_cumulative)[14] <- "RollingAverage_Total_Mortality_Rate"
colnames(cs_cumulative)[15] <- "RollingAverage_New_Mortality_Rate"
colnames(cs_cumulative)[16] <- "EIU_Democracy"
cs_cumulative$New_Case_Rate = cs_cumulative$New_Case_Country/cs_cumulative$Population
cs_cumulative <- cs_cumulative %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(RollingAverage_Total_Case_Country = frollmean(Total_Case_Country, 7, na.rm=TRUE))
cs_cumulative <- cs_cumulative %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(RollingAverage_Total_Death_Country = frollmean(Total_Death_Country, 7,na.rm=TRUE))
cs_cumulative <- cs_cumulative %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(RollingAverage_Total_Case_Rate = frollmean(Total_Case_Rate, 7,na.rm=TRUE))
cs_cumulative <- cs_cumulative %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(RollingAverage_New_Case_Rate = frollmean(New_Case_Rate, 7,na.rm=TRUE))
cs_cumulative <- cs_cumulative %>%
dplyr::select(colnames(cs_cumulative)[c(1:8,11,81,9,10,82,12,83,13,84,85,14,15)], everything())
cs_cumulative <- cs_cumulative %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(Total_Mortality_Rate_Growth = log(RollingAverage_Total_Mortality_Rate) - log(lag(RollingAverage_Total_Mortality_Rate,7)))
cs_cumulative$respiratory_deaths_rate <- 1/3*(cs_cumulative$respiratory_deaths/cs_cumulative$Population)
colnames(cs_cumulative)
## [1] "COUNTRY"
## [2] "X1"
## [3] "Date"
## [4] "Total_Case_Country"
## [5] "Total_Death_Country"
## [6] "New_Case_Country"
## [7] "New_Death_Country"
## [8] "Population"
## [9] "Total_Case_Rate"
## [10] "risktaking"
## [11] "Total_Mortality_Rate"
## [12] "New_Mortality_Rate"
## [13] "posrecip"
## [14] "RollingAverage_New_Case_Country"
## [15] "negrecip"
## [16] "RollingAverage_New_Death_Country"
## [17] "altruism"
## [18] "trust"
## [19] "RollingAverage_Total_Mortality_Rate"
## [20] "RollingAverage_New_Mortality_Rate"
## [21] "EIU_Democracy"
## [22] "longitude"
## [23] "C1_School.closing"
## [24] "C1_Flag"
## [25] "C2_Workplace.closing"
## [26] "C2_Flag"
## [27] "C3_Cancel.public.events"
## [28] "C3_Flag"
## [29] "C4_Restrictions.on.gatherings"
## [30] "C4_Flag"
## [31] "C5_Close.public.transport"
## [32] "C5_Flag"
## [33] "C6_Stay.at.home.requirements"
## [34] "C6_Flag"
## [35] "C7_Restrictions.on.internal.movement"
## [36] "C7_Flag"
## [37] "C8_International.travel.controls"
## [38] "E1_Income.support"
## [39] "E1_Flag"
## [40] "E2_Debt.contract.relief"
## [41] "E3_Fiscal.measures"
## [42] "H1_Public.information.campaigns"
## [43] "H1_Flag"
## [44] "H2_Testing.policy"
## [45] "H3_Contact.tracing"
## [46] "H4_Emergency.investment.in.healthcare"
## [47] "H5_Investment.in.vaccines"
## [48] "M1_Wildcard"
## [49] "StringencyIndex"
## [50] "LegacyStringencyIndex"
## [51] "Oxford_Cases"
## [52] "Oxford_Deaths"
## [53] "Latitude"
## [54] "Longitude"
## [55] "prop65"
## [56] "propurban"
## [57] "popdensity"
## [58] "driving"
## [59] "walking"
## [60] "transit"
## [61] "arrivals"
## [62] "departures"
## [63] "vul_emp"
## [64] "GNI"
## [65] "health_exp"
## [66] "pollution"
## [67] "EUI_democracy"
## [68] "freedom_house"
## [69] "cellular_sub"
## [70] "milex"
## [71] "milper"
## [72] "irst"
## [73] "pec"
## [74] "cinc"
## [75] "endocrine_deaths"
## [76] "blood_pressure_deaths"
## [77] "respiratory_deaths"
## [78] "govt_accountability"
## [79] "political_stability"
## [80] "govt_effectiveness"
## [81] "regulatory_quality"
## [82] "rule_of_law"
## [83] "control_of_corruption"
## [84] "isocode"
## [85] "patience"
## [86] "wm.trust"
## [87] "New_Case_Rate"
## [88] "RollingAverage_Total_Case_Country"
## [89] "RollingAverage_Total_Death_Country"
## [90] "RollingAverage_Total_Case_Rate"
## [91] "RollingAverage_New_Case_Rate"
## [92] "Total_Mortality_Rate_Growth"
## [93] "respiratory_deaths_rate"
first.date.mortality<-c(0)
first.date.case<-c(0)
peak.date.mortality<-c(0)
peak.date.case<-c(0)
days.to.peak.mortality<-c(0)
days.to.peak.case<-c(0)
peak.or.no.mortality<-c(0)
peak.or.no.case<-c(0)
country <- c("")
peak.mortality<-c(0)
peak.case<-c(0)
early.mortality<-c(0)
early.case<-c(0)
peak.new.mortality<-c(0)
peak.new.case<-c(0)
early.stringency<-c(0)
peak.stringency<-c(0)
peak.date.stringency<-c(0)
early.mobility.walking<-c(0)
early.mortality.growth<-c(0)
early.case.growth<-c(0)
dems_data<-aggregate(data=cs_cumulative,cbind(prop65,propurban,popdensity,vul_emp,health_exp,GNI,pollution,Latitude,Longitude,EIU_Democracy,freedom_house)~COUNTRY,FUN=mean,na.rm=T)
for(i in 1:length(unique(dems_data$COUNTRY))){
bb<-subset(cs_cumulative,COUNTRY==unique(dems_data$COUNTRY)[i])
country[i] <- as.character(unique(dems_data$COUNTRY)[i])
bb<-bb[order(bb$Date),]
bb<-bb[order(bb$COUNTRY),]
bb <- bb %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(RollingAverage_Walking = frollmean(walking, 7))
bb <- bb %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(RollingAverage_StringencyIndex = frollmean(StringencyIndex, 7))
peak.new.mortality[i]<-max(bb$RollingAverage_New_Mortality_Rate,na.rm = TRUE) # Peak new mortality
peak.new.case[i]<-max(bb$RollingAverage_New_Case_Rate, na.rm = TRUE) # Peak new case
peak.date.mortality[i]<-as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate==peak.new.mortality[i]))]) # Peak date of new mortality
first.date.mortality[i]<-as.character(bb$Date[which(bb$Total_Death_Country>0)[1]]) # Date of first death
peak.mortality[i]<-bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate==peak.new.mortality[i])] # Total mortality rate at the peak
peak.date.case[i]<-as.character(bb$Date[which((bb$RollingAverage_New_Case_Rate==peak.new.case[i]))]) # Peak date of new case
first.date.case[i]<-as.character(bb$Date[which(bb$Total_Case_Country>0)[1]]) # Date of first case
peak.case[i]<-bb$RollingAverage_Total_Case_Rate[which.max(bb$RollingAverage_New_Case_Rate==peak.new.case[i])] # Toal case rate at the peak
days.to.peak.mortality[i]<-as.Date(peak.date.mortality[i])-as.Date(first.date.mortality[i])
peak.or.no.mortality[i]<-ifelse(peak.date.mortality[i]==max(bb$Date),0,1)
days.to.peak.case[i]<-as.Date(peak.date.case[i])-as.Date(first.date.case[i])
peak.or.no.case[i]<-ifelse(peak.date.case[i]==max(bb$Date),0,1)
early.mortality[i]<-bb$RollingAverage_Total_Mortality_Rate[which(bb$Total_Death_Country>0)[7]] # Total death rate in the first week since first death
early.mortality.growth[i]<-log(bb$RollingAverage_New_Mortality_Rate[which(bb$Total_Death_Country>0)[7]]) - log(bb$RollingAverage_New_Mortality_Rate[which(bb$Total_Death_Country>0)[1]])# Growth rate of new mortality rate in the first week since first death
early.case[i]<-bb$RollingAverage_Total_Case_Rate[which(bb$Total_Case_Country>0)[7]] # Total case rate in the first week since first case
if (bb$RollingAverage_New_Case_Rate[which(bb$Total_Case_Country>0)[1]] == 0){
early.case.growth[i]<-log(bb$RollingAverage_New_Case_Rate[which(bb$Total_Case_Country>0)[7]]) - log(bb$RollingAverage_Total_Case_Rate[which(bb$Total_Case_Country>0)[1]])# Growth rate of new case rate in the first week since first case
}else{
early.case.growth[i]<-log(bb$RollingAverage_New_Case_Rate[which(bb$Total_Case_Country>0)[7]]) - log(bb$RollingAverage_New_Case_Rate[which(bb$Total_Case_Country>0)[1]])# Growth rate of new case rate in the first week since first case
}
early.mobility.walking[i]<-bb$RollingAverage_Walking[which(bb$Total_Death_Country>0)[1]-1] # Weekly average walking mobility in the week prior to first death
early.stringency[i]<-bb$RollingAverage_StringencyIndex[which(bb$Total_Death_Country>0)[1]-7] #Weekly average stringency index in the week prior to first death
peak.stringency[i]<-max(bb$StringencyIndex,na.rm=T)
if (peak.stringency[i] == -Inf){
peak.date.stringency[i] <- -Inf
}else{
peak.date.stringency[i]<-as.character(bb$Date[which.max(bb$StringencyIndex)])
}
}
## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length
## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length
## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length
## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length
## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length
## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length
## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in max(bb$StringencyIndex, na.rm = T): no non-missing arguments to max;
## returning -Inf
## Warning in max(bb$StringencyIndex, na.rm = T): no non-missing arguments to max;
## returning -Inf
## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length
## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in max(bb$StringencyIndex, na.rm = T): no non-missing arguments to max;
## returning -Inf
## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length
## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length
## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length
## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length
## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.date.case[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Case_Rate == : number of items
## to replace is not a multiple of replacement length
stringency.growth.to.max<-(peak.stringency-early.stringency)/as.numeric(as.Date(peak.date.stringency)-as.Date(first.date.mortality))
log.peak.mortality.to.duration <- log(peak.new.mortality)/as.numeric(days.to.peak.mortality)
log.peak.case.to.duration <- log(peak.new.case)/as.numeric(days.to.peak.case)
# set NA early stringency to 0
# early.string[is.na(early.string)]<-0
surv.dat<-data.frame(country,days.to.peak.mortality,peak.or.no.mortality,
days.to.peak.case,peak.or.no.case,
early.mortality,early.mortality.growth,
early.case,early.case.growth,
peak.mortality,peak.case,peak.new.mortality,peak.new.case,
log.peak.mortality.to.duration,log.peak.case.to.duration,
early.stringency,stringency.growth.to.max,peak.stringency,early.mobility.walking,dems_data[,-1])
#remove vietnam
surv.dat<-surv.dat[-nrow(surv.dat),]
surv.dat<-mutate(surv.dat,stringent=ifelse(early.stringency>19,"SI>19","SI<19"),stringent=factor(stringent))
km_fit <- survfit(Surv(days.to.peak.case,peak.or.no.case) ~1, data=surv.dat)
p1<-autoplot(km_fit)+theme_bw()+xlab("Number of Days")+ylab("Probability Case Peak is Yet to Come")
km_fit2<-survfit(Surv(days.to.peak.case,peak.or.no.case)~stringent, data=surv.dat)
p2<-autoplot(km_fit2)+theme_bw()+xlab("Number of Days")+ylab("Probability Case Peak is Yet to Come")+theme(legend.position = c(0.2, 0.2),legend.title=element_blank())
p3<-ggplot(data=surv.dat,aes(x=early.stringency,fill=stringent))+geom_histogram(color="black",alpha=.5)+theme_bw()+xlab("Average SI in the Week Prior to First Death")+ylab("Frequency of Countries")+theme(legend.title = element_blank(),legend.position = c(0.8,0.6))
g <- grid.arrange(p1, p2, p3, nrow=1)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (stat_bin).
# g <- arrangeGrob(p1,p2,p3,nrow=1)
ggsave("Figure_cox_case_appendix.png", g, width = 15, height = 7)
km_fit <- survfit(Surv(days.to.peak.mortality,peak.or.no.mortality) ~1, data=surv.dat)
p1<-autoplot(km_fit)+theme_bw()+xlab("Number of Days")+ylab("Probability Mortality Peak is Yet to Come")
km_fit2<-survfit(Surv(days.to.peak.mortality,peak.or.no.mortality)~stringent, data=surv.dat)
p2<-autoplot(km_fit2)+theme_bw()+xlab("Number of Days")+ylab("Probability Mortality Peak is Yet to Come")+theme(legend.position = c(0.2, 0.2),legend.title=element_blank())
p3<-ggplot(data=surv.dat,aes(x=early.stringency,fill=stringent))+geom_histogram(color="black",alpha=.5)+theme_bw()+xlab("Average SI in the Week Prior to First Death")+ylab("Frequency of Countries")+theme(legend.title = element_blank(),legend.position = c(0.8,0.6))
g <- grid.arrange(p1, p2, p3, nrow=1)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (stat_bin).
# g <- arrangeGrob(p1,p2,p3,nrow=1)
ggsave("Figure_cox_mortality_appendix.png", g, width = 15, height = 7)
cox.death<-coxph(Surv(days.to.peak.mortality,peak.or.no.mortality) ~
log(peak.new.mortality)+log(early.mortality)+early.mortality.growth+
early.stringency+stringency.growth.to.max+early.mobility.walking+
prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat)
cox.case<-coxph(Surv(days.to.peak.case,peak.or.no.case) ~
log(peak.new.case)+log(early.case)+early.case.growth+
early.stringency+stringency.growth.to.max+early.mobility.walking+
prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat)
test.death<-cox.zph(cox.death)$table[nrow(cox.zph(cox.death)$table),ncol(cox.zph(cox.death)$table)]
test.case<-cox.zph(cox.case)$table[nrow(cox.zph(cox.case)$table),ncol(cox.zph(cox.case)$table)]
stargazer(cox.death,cox.case,type="html",out="Table_cox_reg_output_appendix.htm",
dep.var.labels=c("Survival Probability of Mortality Peaking at Time (t)", "Survival Probability of Case Peaking at Time (t)"),
covariate.labels = c("Log(Peak Mortality)", "Log(Early Mortality)", "Early Mortality Growth",
"Log(Peak Case)", "Log(Early Case)", "Early Case Growth",
"Early Stringency","Stringency Growth to Maximum", "Early Mobility",
"Prop. 65+","Prop. Urban","Pop. Density","Vulnerable Emp.",
"Log(GNI)","EIU Democracy","Latitude-Longitude"),
df = FALSE,omit.stat =c("max.rsq","logrank"),
notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","PH Test refers to testing the proportional hazards assumption (Grambsch and Therneau (1994)).","Null hypothesis is the assumption is not violated."),
notes.append=F,notes.align ="l",
title=("Cox Proportional Hazards Regression: Duration to Peak Mortality (Appendix)"),
add.lines = list(c("PH Test p-value",round(test.death,3),round(test.case,3))))
### - peak cum. mortality -
cs.total.mortality<- lm(log(peak.mortality) ~
log(early.mortality)+early.mortality.growth+
early.stringency+stringency.growth.to.max+early.mobility.walking+
prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat)
se.total.mortality<-coeftest(cs.total.mortality, vcov = vcovHC(cs.total.mortality, type = "HC1"))
### - peak new mortality -
cs.new.mortality<- lm(log(peak.new.mortality) ~
log(early.mortality)+early.mortality.growth+
early.stringency+stringency.growth.to.max+early.mobility.walking+
prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat)
se.new.mortality<-coeftest(cs.new.mortality, vcov = vcovHC(cs.new.mortality, type = "HC1"))
### - peak new mortality to duration ratio -
cs.peak.duration.ratio<- lm(log.peak.mortality.to.duration ~
log(early.mortality)+early.mortality.growth+
early.stringency+stringency.growth.to.max+early.mobility.walking+
prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat)
se.peak.duration.ratio<-coeftest(cs.peak.duration.ratio, vcov = vcovHC(cs.peak.duration.ratio, type = "HC1"))
stargazer(cs.total.mortality,cs.new.mortality,cs.peak.duration.ratio,
se=list(se.total.mortality[,2],se.new.mortality[,2],se.peak.duration.ratio[,2]),type="html",out="Table_cs_reg_output_appendix.htm",intercept.bottom = FALSE,
dep.var.labels=c("Log(Peak Cum. Mortality Rate)", "Log(Peak New Mortality Rate)", "Peak New Moratlity Rate to Duration Rato"),
covariate.labels=c("Intercept","Log(Early Mortality)","Early Mortality Growth",
"Early Stringency","Stringency Growth to Maximum", "Early Mobility",
"Prop. 65+","Prop. Urban","Pop. Density","Vulnerable Emp.", "Log(GNI)",
"EIU Democracy","Latitude-Longitude"),
df = FALSE,notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","Heteroscedastic-Robust standard errors"),
notes.append=F,notes.align ="l",font.size = "tiny",
title=("Cross-Country Regression: Explaining Differences in First-Wave Mortality Rates (Appendix)"))
df.definition <- data.frame("Variable" = c("Cum. Mortality Rate", "New Mortality Rate",
"Early Mortality", "Early Mortality Growth", "Peak Cum. Mortality", "Peak New Mortality",
"PD to Peak Mortality", "Logged Peak Mortality-to-PD",
"Early Confirmed Case", "Early Confirmed Case Growth", "Peak Cum. Confirmed Case", "Peak New Confirmed Case",
"PD to Peak Confirmed Case", "Logged Peak Case-to-PD",
"Early Stringency", "Stringency Delta", "Peak Stringency",
"Early Mobility",
"Prop. 65+", "Prop. Urban", "Pop. Density",
"Vulnerable Employment","Health Expenditure","Log(GNI)","Pollution",
"Tourist Arrivals", "Tourist Departures",
"Latitude","Longitude","Democracy"),
"Definition" = c("7-day rolling average of cumulative mortality rate out of the total population",
"7-day rolling average of daily new mortality rate out ot the total population",
"Cumulative mortality rate in the week following the first death",
"Growth rate of new mortality rate in the week following the first death",
"Cumulative mortality rate at the peak of new mortality rate in the first quasi-bell curve",
"New mortality rate at the peak of new mortality rate in the first quasi-bell curve",
"Day-to-peak of new mortality rate in the first quasi-bell curve",
"The ratio of the logged peak new mortality rate to the PD to peak mortality",
"Cumulative confirmed case rate in the week following the first case",
"Growth rate of new confirmed case rate in the week following the first case",
"Cumulative confirmed case rate at the peak of new confirmed case rate in the first quasi-bell curve",
"New confirmed case rate at the peak of new confirmed case rate in the first quasi-bell curve",
"Day-to-peak of new confirmed case rate in the first quasi-bell curve",
"The ratio of the logged peak new confirmed case rate to the PD to peak confirmed case",
"Weekly average stringency index in the week prior to the first death",
"Growth rate of SI from the week prior to the first death to the maximum level of stringency",
"Maximum level of stringency index in the first quasi-bell curve",
"Weekly average level of mobility in terms of walking in the week prior to the first death, reported by Apple",
"Elderly population (people aged 65 and over) as a percentage of the total population",
"Urban population as a percentage of the total population",
"Midyear population divided by land area in square kilometers",
"Employment in vulnerable sectors (i.e., family workers and own-account workers) as a percentage of the total employment",
"Level of current health expenditure (including healthcare goods and services) as a percentage of GDP",
"The logged gross national income per capita",
"Population-weighted exposure to ambient PM2.5 pollution",
"International inbound tourists to the country",
"International outbound tourists from the country",
"Latitude coordinate of the country",
"Longitude coordinate of the country",
"The Democracy index calculated by The Economist Intelligence Unit"
),
"Source" = c("Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on JHU COVID-19 Data",
"Authors' calculation based on OxCGRT Data",
"Authors' calculation based on OxCGRT Data",
"Authors' calculation based on OxCGRT Data",
"Authors' calculation based on Apple COVID-19 Mobility Trends Reports",
"World Development Indicators",
"World Development Indicators",
"World Development Indicators",
"World Development Indicators",
"World Development Indicators",
"World Development Indicators",
"World Development Indicators",
"World Development Indicators",
"World Development Indicators",
"Country-level coordinates from Google",
"Country-level coordinates from Google",
"The EIU Democracy Index 2019 Database"
)
)
rownames(df.definition) <- NULL
stargazer(df.definition,type="html",rownames = FALSE, summary = FALSE, out="Table_variable_definition.htm")
##
## <table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Variable</td><td>Definition</td><td>Source</td></tr>
## <tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Rate</td><td>7-day rolling average of cumulative mortality rate out of the total population</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">New Mortality Rate</td><td>7-day rolling average of daily new mortality rate out ot the total population</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Early Mortality</td><td>Cumulative mortality rate in the week following the first death</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Early Mortality Growth</td><td>Growth rate of new mortality rate in the week following the first death</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Peak Cum. Mortality</td><td>Cumulative mortality rate at the peak of new mortality rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Peak New Mortality</td><td>New mortality rate at the peak of new mortality rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">PD to Peak Mortality</td><td>Day-to-peak of new mortality rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Logged Peak Mortality-to-PD</td><td>The ratio of the logged peak new mortality rate to the PD to peak mortality</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Early Confirmed Case</td><td>Cumulative confirmed case rate in the week following the first case</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Early Confirmed Case Growth</td><td>Growth rate of new confirmed case rate in the week following the first case</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Peak Cum. Confirmed Case</td><td>Cumulative confirmed case rate at the peak of new confirmed case rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Peak New Confirmed Case</td><td>New confirmed case rate at the peak of new confirmed case rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">PD to Peak Confirmed Case</td><td>Day-to-peak of new confirmed case rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Logged Peak Case-to-PD</td><td>The ratio of the logged peak new confirmed case rate to the PD to peak confirmed case</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Early Stringency</td><td>Weekly average stringency index in the week prior to the first death</td><td>Authors' calculation based on OxCGRT Data</td></tr>
## <tr><td style="text-align:left">Stringency Delta</td><td>Growth rate of SI from the week prior to the first death to the maximum level of stringency</td><td>Authors' calculation based on OxCGRT Data</td></tr>
## <tr><td style="text-align:left">Peak Stringency</td><td>Maximum level of stringency index in the first quasi-bell curve</td><td>Authors' calculation based on OxCGRT Data</td></tr>
## <tr><td style="text-align:left">Early Mobility</td><td>Weekly average level of mobility in terms of walking in the week prior to the first death, reported by Apple</td><td>Authors' calculation based on Apple COVID-19 Mobility Trends Reports</td></tr>
## <tr><td style="text-align:left">Prop. 65+</td><td>Elderly population (people aged 65 and over) as a percentage of the total population</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Prop. Urban</td><td>Urban population as a percentage of the total population</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Pop. Density</td><td>Midyear population divided by land area in square kilometers</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Vulnerable Employment</td><td>Employment in vulnerable sectors (i.e., family workers and own-account workers) as a percentage of the total employment</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Health Expenditure</td><td>Level of current health expenditure (including healthcare goods and services) as a percentage of GDP</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Log(GNI)</td><td>The logged gross national income per capita</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Pollution</td><td>Population-weighted exposure to ambient PM2.5 pollution</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Tourist Arrivals</td><td>International inbound tourists to the country</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Tourist Departures</td><td>International outbound tourists from the country</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Latitude</td><td>Latitude coordinate of the country</td><td>Country-level coordinates from Google</td></tr>
## <tr><td style="text-align:left">Longitude</td><td>Longitude coordinate of the country</td><td>Country-level coordinates from Google</td></tr>
## <tr><td style="text-align:left">Democracy</td><td>The Democracy index calculated by The Economist Intelligence Unit</td><td>The EIU Democracy Index 2019 Database</td></tr>
## <tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr></table>
### Creating new weekly mortality growth and running libraries.
library(lubridate)
library(zoo)
library(quantmod)
library(fBasics)
library(tseries)
library(sandwich)
library(lmtest)
library(lattice)
library(xtable)
library(vars)
library(plyr)
library(dplyr)
library(gridExtra)
library(corrplot)
library(ggplot2)
library(reshape2)
library(data.table)
library(rvest)
library(plm)
library(stringr)
library(tidyverse)
library(foreign)
library(stargazer)
library(haven)
# Remove factor variables for government responses
datweekly_panel<-Final_Data_Country
datweekly_panel<-datweekly_panel %>% dplyr::select(-contains("Flag"))
datweekly_panel<-datweekly_panel %>% dplyr::select(-contains("Lagged"))
datweekly_panel$latitude <- NULL
datweekly_panel$longitude <- NULL
gps.country <- read_dta("data/global_preference_survey_country.dta")
# Change country names for merge
gps.country[which(gps.country$country == "South Korea"),"country"] <- "Korea, South"
gps.country[which(gps.country$country == "Czech Republic"),"country"] <- "Czechia"
gps.country[which(gps.country$country == "United States"),"country"] <- "US"
gps.individual <- read_dta("data/global_preference_survey_individual.dta")
# Change country names for merge
gps.individual[which(gps.individual$country == "South Korea"),"country"] <- "Korea, South"
gps.individual[which(gps.individual$country == "Czech Republic"),"country"] <- "Czechia"
gps.individual[which(gps.individual$country == "United States"),"country"] <- "US"
gps.individual$age <- as.character(gps.individual$age)
gps.individual[which(gps.individual$age == "99 99+"), "age"] <- "99"
gps.individual[which(gps.individual$age == "100 (Refused"), "age"] <- NA
gps.individual$age <- as.numeric(gps.individual$age)
gps.individual.pop65 <- gps.individual[which(!is.na(gps.individual$age) & gps.individual$age > 65), ]
gps.individual.pop65 <- gps.individual.pop65[which(!is.na(gps.individual.pop65$trust)),]
gps.country.pop65 <- ddply(gps.individual.pop65, "country", function(X) data.frame(wm.trust = weighted.mean(X$trust, X$wgt)))
colnames(gps.country)[1] <- "COUNTRY"
datweekly_panel <- merge(datweekly_panel, gps.country, by=c("COUNTRY"), all.x=TRUE)
colnames(gps.country.pop65)[1] <- "COUNTRY"
datweekly_panel <- merge(datweekly_panel, gps.country.pop65, by=c("COUNTRY"), all.x=TRUE)
datweekly_panel<-pdata.frame(datweekly_panel, index = c("COUNTRY", "Date"))
datweekly_panel$Date<-as.Date(datweekly_panel$Date,"%Y-%m-%d")
#reorder
datweekly_panel<-datweekly_panel[order(datweekly_panel$Date),]
datweekly_panel<-datweekly_panel[order(datweekly_panel$COUNTRY),]
colnames(datweekly_panel)[colnames(datweekly_panel) == "Total_Cases_Country"] <- "Total_Case_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "Total_Deceased_Country"] <- "Total_Death_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "New_Confirmed_Country"] <- "New_Case_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "New_Total_Deceased_Country"] <- "New_Death_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "Total_Mortality_Rate_Per_Capita"] <- "Total_Mortality_Rate"
colnames(datweekly_panel)[colnames(datweekly_panel) == "New_Mortality_Rate_Per_Capita"] <- "New_Mortality_Rate"
colnames(datweekly_panel)[colnames(datweekly_panel) == "Total_Cases_Country_Per_Capita"] <- "Total_Case_Rate"
colnames(datweekly_panel)[colnames(datweekly_panel) == "rolling_average_confirmed"] <- "RollingAverage_New_Case_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "rolling_average_deceased"] <- "RollingAverage_New_Death_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "total_rolling_average_mortality"] <- "RollingAverage_Total_Mortality_Rate"
colnames(datweekly_panel)[colnames(datweekly_panel) == "new_rolling_average_mortality"] <- "RollingAverage_New_Mortality_Rate"
colnames(datweekly_panel)[colnames(datweekly_panel) == "EUI_democracy"] <- "EIU_Democracy"
datweekly_panel$RollingAverage_Total_Mortality_Rate <- NULL
datweekly_panel$longitude <- NULL
datweekly_panel$latitude <- NULL
datweekly_panel$New_Case_Rate = datweekly_panel$New_Case_Country/datweekly_panel$Population
datweekly_panel <- datweekly_panel %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(RollingAverage_New_Case_Rate = frollmean(New_Case_Rate, 7,na.rm=TRUE))
datweekly_panel <- datweekly_panel %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(Total_Mortality_Rate_Growth = log(Total_Mortality_Rate) - Lag(log(Total_Mortality_Rate),7))
datweekly_panel <- datweekly_panel %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(Total_Case_Rate_Growth = log(Total_Case_Rate) - Lag(log(Total_Case_Rate),7))
datweekly_panel <- datweekly_panel %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(New_Case_Rate_Growth = log(RollingAverage_New_Case_Rate) - Lag(log(RollingAverage_New_Case_Rate),7))
datweekly_panel <- datweekly_panel %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(New_Mortality_Rate_Growth = log(RollingAverage_New_Mortality_Rate) - Lag(log(RollingAverage_New_Mortality_Rate),7))
datweekly_panel$respiratory_deaths_rate <- 1/3*(datweekly_panel$respiratory_deaths/datweekly_panel$Population)
### Reorder columns
datweekly_panel <- datweekly_panel %>%
dplyr::select(c("COUNTRY", "X1", "Date", "Total_Case_Country", "Total_Death_Country", "New_Case_Country", "New_Death_Country", "Population", "Total_Case_Rate", "Total_Mortality_Rate", "New_Case_Rate", "New_Mortality_Rate", "RollingAverage_New_Case_Country", "RollingAverage_New_Death_Country", "RollingAverage_New_Case_Rate", "RollingAverage_New_Mortality_Rate", "Total_Case_Rate_Growth", "Total_Mortality_Rate_Growth", "New_Case_Rate_Growth", "New_Mortality_Rate_Growth"), everything())
datweekly_panel$Total_Case_Rate_Growth[is.nan(datweekly_panel$Total_Case_Rate_Growth)] <- NA
datweekly_panel$Total_Mortality_Rate_Growth[is.nan(datweekly_panel$Total_Mortality_Rate_Growth)] <- NA
datweekly_panel$New_Case_Rate_Growth[is.nan(datweekly_panel$New_Case_Rate_Growth)] <- NA
datweekly_panel$New_Mortality_Rate_Growth[is.nan(datweekly_panel$New_Mortality_Rate_Growth)] <- NA
datweekly_panel$New_Mortality_Rate_Growth[is.infinite(-datweekly_panel$New_Mortality_Rate_Growth)] <- Inf
colnames(datweekly_panel)
## [1] "COUNTRY"
## [2] "X1"
## [3] "Date"
## [4] "Total_Case_Country"
## [5] "Total_Death_Country"
## [6] "New_Case_Country"
## [7] "New_Death_Country"
## [8] "Population"
## [9] "Total_Case_Rate"
## [10] "Total_Mortality_Rate"
## [11] "New_Case_Rate"
## [12] "New_Mortality_Rate"
## [13] "RollingAverage_New_Case_Country"
## [14] "RollingAverage_New_Death_Country"
## [15] "RollingAverage_New_Case_Rate"
## [16] "RollingAverage_New_Mortality_Rate"
## [17] "Total_Case_Rate_Growth"
## [18] "Total_Mortality_Rate_Growth"
## [19] "New_Case_Rate_Growth"
## [20] "New_Mortality_Rate_Growth"
## [21] "C1_School.closing"
## [22] "C2_Workplace.closing"
## [23] "C3_Cancel.public.events"
## [24] "C4_Restrictions.on.gatherings"
## [25] "C5_Close.public.transport"
## [26] "C6_Stay.at.home.requirements"
## [27] "C7_Restrictions.on.internal.movement"
## [28] "C8_International.travel.controls"
## [29] "E1_Income.support"
## [30] "E2_Debt.contract.relief"
## [31] "E3_Fiscal.measures"
## [32] "H1_Public.information.campaigns"
## [33] "H2_Testing.policy"
## [34] "H3_Contact.tracing"
## [35] "H4_Emergency.investment.in.healthcare"
## [36] "H5_Investment.in.vaccines"
## [37] "M1_Wildcard"
## [38] "StringencyIndex"
## [39] "LegacyStringencyIndex"
## [40] "Oxford_Cases"
## [41] "Oxford_Deaths"
## [42] "Latitude"
## [43] "Longitude"
## [44] "prop65"
## [45] "propurban"
## [46] "popdensity"
## [47] "driving"
## [48] "walking"
## [49] "transit"
## [50] "arrivals"
## [51] "departures"
## [52] "vul_emp"
## [53] "GNI"
## [54] "health_exp"
## [55] "pollution"
## [56] "EIU_Democracy"
## [57] "freedom_house"
## [58] "cellular_sub"
## [59] "milex"
## [60] "milper"
## [61] "irst"
## [62] "pec"
## [63] "cinc"
## [64] "endocrine_deaths"
## [65] "blood_pressure_deaths"
## [66] "respiratory_deaths"
## [67] "govt_accountability"
## [68] "political_stability"
## [69] "govt_effectiveness"
## [70] "regulatory_quality"
## [71] "rule_of_law"
## [72] "control_of_corruption"
## [73] "isocode"
## [74] "patience"
## [75] "risktaking"
## [76] "posrecip"
## [77] "negrecip"
## [78] "altruism"
## [79] "trust"
## [80] "wm.trust"
## [81] "respiratory_deaths_rate"
datweekly_panel <- datweekly_panel %>% dplyr::ungroup()
datweekly_panel<-pdata.frame(datweekly_panel, index = c("COUNTRY", "Date"))
datweekly_panel<-datweekly_panel[order(datweekly_panel$Date),]
datweekly_panel<-datweekly_panel[order(datweekly_panel$COUNTRY),]
###baseline projection regressions, remove 'infinite' values. two-way FE, HAC robust SEs, clustered on country
regmoda<-plm(New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))
regmodb<-plm(New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))
regmodc<-plm(New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))
regmodd<-plm(New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))
stargazer(digits=6,regmoda,regmodb,regmodc,regmodd,type="html",se=list(se.a[,2],se.b[,2],se.c[,2],se.d[,2]), out="Table_panel_reg_output_weekly.htm",
dep.var.labels=c("Weekly Avg. New Mortality Growth (t)"),
covariate.labels=c("New Mortality Growth (t-7)","Stringency (t-14)","New Mortality Growth (t-14)","Stringency (t-21)","New Mortality Growth (t-21)","Stringency (t-28)","New Mortality Growth (t-28)","Stringency (t-35)"), df = FALSE,omit.stat="adj.rsq",notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","HAC robust standard errors, clustered by country. Time and Country FEs."),notes.append=F,notes.align ="l",title="Table 1. Mortality Projection - Average Impact",add.lines = list(c("Fixed effects?", "Y", "Y","Y","Y")))
### plot of 10-unit increase in Stringency effects over time on future mortality growth
p.dat_weekly<-c(coef(regmoda)[2],coef(regmodb)[2],coef(regmodc)[2],coef(regmodd)[2])
p.dat_weekly<-cbind(p.dat_weekly,p.dat_weekly+1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
p.dat_weekly<-cbind(p.dat_weekly,p.dat_weekly[,1]-1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
colnames(p.dat_weekly)<-c("mean","upper","lower")
p.dat_weekly<-data.frame(p.dat_weekly)*10*100
Plot_59 <-ggplot(data=p.dat_weekly,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean))+geom_point(size=4,shape=21,fill="grey")+geom_hline(yintercept=0,linetype=2)+xlab("Increase in Stringency Index of 10 units")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+geom_errorbar(aes(ymin=lower,ymax=upper))
Plot_59
Plot_60 <-ggplot(data=p.dat_weekly,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean))+geom_point(size=4,shape=21,fill="grey")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+geom_errorbar(aes(ymin=lower,ymax=upper))+ggtitle("Average Impact")
Plot_60
###baseline projection regressions, remove 'infinite' values. two-way FE, HAC robust SEs, clustered on country
var.list <- c("prop65", "propurban", "Latitude", "Longitude", "popdensity", "arrivals", "departures", "vul_emp", "GNI", "health_exp", "pollution", "EIU_Democracy", "trust", "risktaking")
label.list <- c("Proportion 65+", "Proportion Urban", "Latitude", "Longitude", "Population Density", "Log(Arrivals)", "Log(Departures)", "Vulnerable Employees", "Log(GNI per capita)", "Health Expenditures", "Pollution", "EIU Democracy", "Trust", "Risk Taking")
p.dat_weekly.list <- list()
s.list <- list()
for (i in c(1:length(var.list))){
var <- var.list[i]
label <- label.list[i]
if (var == "arrivals" || var == "departures" || var == "GNI"){
regmoda<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14))+Lag(StringencyIndex,c(14)):","log(",var,")")),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))
regmodb<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21))+Lag(StringencyIndex,c(21)):","log(",var,")")),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))
regmodc<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28))+Lag(StringencyIndex,c(28)):","log(",var,")")),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))
regmodd<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35))+Lag(StringencyIndex,c(35)):","log(",var,")")),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))
}else{
regmoda<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14))+Lag(StringencyIndex,c(14)):",var)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))
regmodb<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21))+Lag(StringencyIndex,c(21)):",var)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))
regmodc<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28))+Lag(StringencyIndex,c(28)):",var)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))
regmodd<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35))+Lag(StringencyIndex,c(35)):",var)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))
}
stargazer(digits=6,regmoda,regmodb,regmodc,regmodd,type="html",
se=list(se.a[,2],se.b[,2],se.c[,2],se.d[,2]),out=paste("Table_panel_",var,"_weekly_reg_output.htm"),
dep.var.labels=c("Weekly Avg. New Mortality Growth (t)"),
dep.var.labels.include = FALSE,
covariate.labels=c("New Mortality Growth (t-7)","Stringency (t-14)",paste("Stringency (t-14) X ", label),"New Mortality Growth (t-14)","Stringency (t-21)", paste("Stringency (t-21) X ", label),"New Mortality Growth (t-21)","Stringency (t-28)",paste("Stringency (t-28) X ", label),"New Mortality Growth (t-28)","Stringency (t-35)",paste("Stringency (t-35) X ", label)),
df = FALSE,omit.stat="adj.rsq",
notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","HAC robust standard errors, clustered by country. Time and Country FEs."),notes.append=F,notes.align ="l",
title=paste("Heterogeneity: ", label),add.lines = list(c("Fixed effects?", "Y", "Y","Y","Y")))
### plot of 10-unit increase in Stringency effects over time on future mortality growth
p.dat<-c(coef(regmoda)[2],coef(regmodb)[2],coef(regmodc)[2],coef(regmodd)[2])
i.dat<-c(coef(regmoda)[3],coef(regmodb)[3],coef(regmodc)[3],coef(regmodd)[3])
effect.dat<-cbind(p.dat, i.dat)
#p.dat_weekly<-cbind(p.dat_weekly,p.dat_weekly+1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
#p.dat_weekly<-cbind(p.dat_weekly,p.dat_weekly[,1]-1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
colnames(effect.dat)<-c("mean.1", "mean.2")
p.dat_weekly.list[[i]]<-data.frame(effect.dat)*10*100
###figure out the 25% percentile and 75% percentile of elderly population rate across countries, to compare effects of a 10-unit rise in stringency level
if (var == "arrivals" || var == "departures" || var == "GNI"){
s.list[[i]] <- summary(unique(log(datweekly_panel[[var]])))
}else{
s.list[[i]] <- summary(unique(datweekly_panel[[var]]))
}
s.list
}
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Proportion 65+</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.261511<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.121918)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.000745</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.004765)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Proportion 65+</td><td>-0.001028<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000303)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.040736</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.048724)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.003301</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003939)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Proportion 65+</td><td></td><td>-0.000570<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000220)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.029495</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.042448)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.005481<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003018)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Proportion 65+</td><td></td><td></td><td>-0.000203</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000272)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.015473</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.043583)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.009380</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.010026)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Proportion 65+</td><td></td><td></td><td></td><td>-0.000338</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000432)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.150457</td><td>0.064602</td><td>0.026340</td><td>0.005453</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>92.330240<sup>***</sup></td><td>27.763570<sup>***</sup></td><td>7.737080<sup>***</sup></td><td>0.953975</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Proportion Urban</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.236219<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.119189)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.006118</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.007232)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Proportion Urban</td><td>-0.000118</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000088)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.054976</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.051941)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.003739</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.006496)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Proportion Urban</td><td></td><td>-0.000104</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000082)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.023535</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.037377)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.007939<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.004088)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Proportion Urban</td><td></td><td></td><td>-0.000006</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000057)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.006323</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.041461)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.004777</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.008539)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Proportion Urban</td><td></td><td></td><td></td><td>-0.000013</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000089)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.120837</td><td>0.056109</td><td>0.025050</td><td>0.003403</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>71.655010<sup>***</sup></td><td>23.896850<sup>***</sup></td><td>7.348300<sup>***</sup></td><td>0.594178</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Latitude</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.246198<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.118344)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.009319<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.005551)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Latitude</td><td>-0.000148<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000043)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.053647</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.052712)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.009554<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003773)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Latitude</td><td></td><td>-0.000042</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000046)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.036790</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.041330)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.004511<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.002604)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Latitude</td><td></td><td></td><td>-0.000105<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000038)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.018244</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.043100)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.007855</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.004937)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Latitude</td><td></td><td></td><td></td><td>-0.000101</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000079)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.129371</td><td>0.054092</td><td>0.030796</td><td>0.006625</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>77.467780<sup>***</sup></td><td>22.988500<sup>***</sup></td><td>9.087527<sup>***</sup></td><td>1.160423</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Longitude</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.231688<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.118265)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.014507<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006002)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Longitude</td><td>0.000009</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000032)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.059887</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.054177)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.010953<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003307)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Longitude</td><td></td><td>0.000040</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000031)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.022741</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.036893)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.008332<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003102)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Longitude</td><td></td><td></td><td>0.000014</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000033)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.000573</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.043903)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.003713</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003585)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Longitude</td><td></td><td></td><td></td><td>0.000055<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000030)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.117473</td><td>0.058134</td><td>0.025541</td><td>0.007858</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>69.394320<sup>***</sup></td><td>24.812490<sup>***</sup></td><td>7.496293<sup>***</sup></td><td>1.378190</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Population Density</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.231754<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.118832)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.014459<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006120)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Population Density</td><td>-0.0000002</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000001)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.059289</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.052100)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.011071<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003273)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Population Density</td><td></td><td>-0.0000001</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000004)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.024046</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.039558)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.008202<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003314)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Population Density</td><td></td><td></td><td>-0.000001</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000012)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.008760</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.039992)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.004394</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.004921)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Population Density</td><td></td><td></td><td></td><td>-0.000003</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000010)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.117253</td><td>0.053016</td><td>0.025076</td><td>0.003531</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>69.247660<sup>***</sup></td><td>22.505740<sup>***</sup></td><td>7.356068<sup>***</sup></td><td>0.616531</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(Arrivals)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.263589<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.124604)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.079898<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.031679)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(Arrivals)</td><td>-0.005681<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.002100)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.041271</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.050360)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.038956<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.017781)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(Arrivals)</td><td></td><td>-0.003006<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.001097)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.036370</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.042291)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.035451</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.031132)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(Arrivals)</td><td></td><td></td><td>-0.002627</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001925)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.028886</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.046779)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.080781</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.051125)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(Arrivals)</td><td></td><td></td><td></td><td>-0.004594</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.002868)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.147066</td><td>0.062341</td><td>0.031470</td><td>0.017067</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>89.890560<sup>***</sup></td><td>26.727330<sup>***</sup></td><td>9.292778<sup>***</sup></td><td>3.021141<sup>**</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(Departures)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.231778</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.144257)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.054784</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.035236)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(Departures)</td><td>-0.004288<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.002356)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.060583</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.051637)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.026756</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.023222)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(Departures)</td><td></td><td>-0.002408<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.001411)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.035649</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.042712)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.047318<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.027988)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(Departures)</td><td></td><td></td><td>-0.003410<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001761)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.026784</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.040929)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.113854<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.055900)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(Departures)</td><td></td><td></td><td></td><td>-0.006691<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003203)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,514</td><td>1,202</td><td>893</td><td>583</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.134003</td><td>0.072635</td><td>0.034779</td><td>0.024029</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>71.282650<sup>***</sup></td><td>28.170470<sup>***</sup></td><td>9.332314<sup>***</sup></td><td>3.906484<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Vulnerable Employees</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.234258<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.117743)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.015837<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006286)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Vulnerable Employees</td><td>0.000070</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000099)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.054748</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.051335)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.012609<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003519)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Vulnerable Employees</td><td></td><td>0.000080</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000074)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.027050</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.039010)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.009775<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003842)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Vulnerable Employees</td><td></td><td></td><td>0.000073</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000078)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.011169</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.041045)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.001574</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003695)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Vulnerable Employees</td><td></td><td></td><td></td><td>0.000139</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000099)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.118373</td><td>0.054715</td><td>0.026329</td><td>0.005970</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>69.997600<sup>***</sup></td><td>23.268420<sup>***</sup></td><td>7.733820<sup>***</sup></td><td>1.044999</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(GNI per capita)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.242076<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.119478)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.017041</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.011580)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(GNI per capita)</td><td>-0.003216<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001298)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.050261</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.050354)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.012260</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.012375)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(GNI per capita)</td><td></td><td>-0.002377<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.001250)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.026257</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.039385)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.001174</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.008215)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(GNI per capita)</td><td></td><td></td><td>-0.000970</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000998)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.009403</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.041592)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.016801</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.018615)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(GNI per capita)</td><td></td><td></td><td></td><td>-0.001279</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001635)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.129493</td><td>0.060706</td><td>0.026220</td><td>0.004594</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>77.551230<sup>***</sup></td><td>25.981200<sup>***</sup></td><td>7.700693<sup>***</sup></td><td>0.802989</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Health Expenditures</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.237573<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.120195)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.010912<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.005384)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Health Expenditures</td><td>-0.000001<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000001)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.056802</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.052433)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.007974<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002921)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Health Expenditures</td><td></td><td>-0.000001<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000001)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.023449</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.038346)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006223<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.002380)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Health Expenditures</td><td></td><td></td><td>-0.000001</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.0000005)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.005785</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.039591)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.005761</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.004756)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Health Expenditures</td><td></td><td></td><td></td><td>-0.000001</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.0000005)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.131083</td><td>0.063997</td><td>0.029219</td><td>0.004849</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>78.647570<sup>***</sup></td><td>27.485730<sup>***</sup></td><td>8.608131<sup>***</sup></td><td>0.847922</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Pollution</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.237309<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.117813)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.018075<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006254)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Pollution</td><td>0.000134<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000029)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.054267</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.050796)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.013989<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003481)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Pollution</td><td></td><td>0.000110<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000049)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.024210</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.037703)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.009278<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003563)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Pollution</td><td></td><td></td><td>0.000034</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000032)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.006080</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.041129)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.003341</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003683)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Pollution</td><td></td><td></td><td></td><td>0.000022</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000042)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.127752</td><td>0.061051</td><td>0.025746</td><td>0.003526</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>76.355910<sup>***</sup></td><td>26.138350<sup>***</sup></td><td>7.557832<sup>***</sup></td><td>0.615741</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: EIU Democracy</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.235646<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.116939)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.002949</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.007507)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X EIU Democracy</td><td>-0.001693<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000706)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.056518</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.051092)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.000692</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.004095)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X EIU Democracy</td><td></td><td>-0.001699<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000672)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.024135</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.038203)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.004520</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.002899)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X EIU Democracy</td><td></td><td></td><td>-0.000545</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000575)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.005643</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.041240)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.002610</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.006496)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X EIU Democracy</td><td></td><td></td><td></td><td>0.000158</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000712)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.126636</td><td>0.063481</td><td>0.025955</td><td>0.003415</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>75.592020<sup>***</sup></td><td>27.249300<sup>***</sup></td><td>7.620978<sup>***</sup></td><td>0.596332</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Trust</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.207884</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.167395)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.016783<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006789)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Trust</td><td>0.003630</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.008616)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.097951</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.066224)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.012771<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003266)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Trust</td><td></td><td>0.005042</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.007544)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.071893<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.039017)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.008255<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003425)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Trust</td><td></td><td></td><td>-0.005469</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.006660)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.004932</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.055082)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.003610</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003581)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Trust</td><td></td><td></td><td></td><td>-0.002255</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.007371)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,376</td><td>1,104</td><td>827</td><td>551</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.119749</td><td>0.068103</td><td>0.028151</td><td>0.002569</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>56.682980<sup>***</sup></td><td>24.018810<sup>***</sup></td><td>6.913423<sup>***</sup></td><td>0.385438</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
##
## <table style="text-align:center"><caption><strong>Heterogeneity: Risk Taking</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.210581</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.164528)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.017332<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006990)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Risk Taking</td><td>-0.010745</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.009817)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.097186</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.061682)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.013928<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002994)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Risk Taking</td><td></td><td>-0.019579<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.007787)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.055504</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.041683)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.009309<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003510)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Risk Taking</td><td></td><td></td><td>-0.007615</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.008346)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>0.007168</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.050674)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.002730</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003350)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Risk Taking</td><td></td><td></td><td></td><td>-0.010828</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.007906)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,376</td><td>1,104</td><td>827</td><td>551</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.122537</td><td>0.078939</td><td>0.027853</td><td>0.004253</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>58.187130<sup>***</sup></td><td>28.168080<sup>***</sup></td><td>6.838061<sup>***</sup></td><td>0.639211</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
#plot
Plot_61 <- ggplot(data=p.dat_weekly.list[[1]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*19.400))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*19.400),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+geom_point(size=4,aes(y=mean.1+mean.2*8.283),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*8.283)),linetype=2)+ggtitle(label.list[1])+ylim(-25,15)
Plot_61
#plot
Plot_62 <- ggplot(data=p.dat_weekly.list[[2]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*86.56))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*86.56),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*66.47),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*66.47)),linetype=2)+ggtitle(label.list[2])+ylim(-25,15)
Plot_62
#plot
Plot_63 <- ggplot(data=p.dat_weekly.list[[3]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*50.83))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*50.83),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*22.01),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*22.01)),linetype=2)+ggtitle(label.list[3])+ylim(-25,15)
Plot_63
#plot
Plot_64 <- ggplot(data=p.dat_weekly.list[[4]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*49.333))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*49.333),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*3.342),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*3.342)),linetype=2)+ggtitle(label.list[4])+ylim(-25,15)
Plot_64
#plot
Plot_65 <- ggplot(data=p.dat_weekly.list[[5]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*223.847))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*223.847),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*33.375),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*33.375)),linetype=2)+ggtitle(label.list[5])+ylim(-25,15)
Plot_65
#plot
Plot_66 <- ggplot(data=p.dat_weekly.list[[6]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.94))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.94),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*15.30),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*15.30)),linetype=2)+ggtitle(label.list[6])+ylim(-25,15)
Plot_66
#plot
Plot_68 <- ggplot(data=p.dat_weekly.list[[7]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.79))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.79),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*15.02),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*15.02)),linetype=2)+ggtitle(label.list[7])+ylim(-25,15)
Plot_68
#plot
Plot_69 <- ggplot(data=p.dat_weekly.list[[8]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*21.797))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*21.797),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*7.407),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*7.407)),linetype=2)+ggtitle(label.list[8])+ylim(-25,15)
Plot_69
Plot_70 <- ggplot(data=p.dat_weekly.list[[9]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*10.747))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*10.747),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*9.242),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*9.242)),linetype=2)+ggtitle(label.list[9])+ylim(-25,15)
Plot_70
Plot_71 <- ggplot(data=p.dat_weekly.list[[10]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*4292.73))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*4292.73),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*542.55),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*542.55)),linetype=2)+ggtitle(label.list[10])+ylim(-25,15)
Plot_71
Plot_72 <- ggplot(data=p.dat_weekly.list[[11]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*23.084))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*23.084),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*10.419),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*10.419)),linetype=2)+ggtitle(label.list[11])+ylim(-25,15)
Plot_72
Plot_73 <- ggplot(data=p.dat_weekly.list[[12]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*8.207))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*8.207),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*6.570),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*6.570)),linetype=2)+ggtitle(label.list[12])+ylim(-25,15)
Plot_73
Plot_74 <- ggplot(data=p.dat_weekly.list[[13]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.28083))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.28083),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*-0.13294),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*-0.13294)),linetype=2)+ggtitle(label.list[13])+ylim(-25,15)
Plot_74
Plot_75 <- ggplot(data=p.dat_weekly.list[[14]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.06918))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.06918),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
geom_point(size=4,aes(y=mean.1+mean.2*-0.15774),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*-0.15774)),linetype=2)+ggtitle(label.list[14])+ylim(-25,15)
Plot_75
Plot_76 <- grid.arrange(Plot_61, Plot_62, Plot_63, Plot_64, Plot_65, Plot_66, Plot_68, Plot_69, Plot_70, Plot_71, Plot_72, ncol=4,nrow=3)
Plot_76
## TableGrob (3 x 4) "arrange": 11 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (1-1,4-4) arrange gtable[layout]
## 5 5 (2-2,1-1) arrange gtable[layout]
## 6 6 (2-2,2-2) arrange gtable[layout]
## 7 7 (2-2,3-3) arrange gtable[layout]
## 8 8 (2-2,4-4) arrange gtable[layout]
## 9 9 (3-3,1-1) arrange gtable[layout]
## 10 10 (3-3,2-2) arrange gtable[layout]
## 11 11 (3-3,3-3) arrange gtable[layout]
# Importing libraries.
library(lubridate)
library(zoo)
library(quantmod)
library(fBasics)
library(tseries)
library(sandwich)
library(lmtest)
library(lattice)
library(xtable)
library(vars)
library(plyr)
library(gridExtra)
library(corrplot)
library(ggplot2)
library(reshape2)
library(data.table)
library(rvest)
library(foreign)
library(dplyr)
library(plm)
library(stringr)
library(stargazer)
library(survival)
library(ggfortify)
library(plotly)
library(sf)
library(readr)
library(mapview)
library(ggplot2)
library(tidyverse)
# library(rlang)
library(reshape)
library(rgdal)
library(lubridate)
library(plotly)
library(patchwork)
library(ggforce)
library(gridExtra)
library(htmltools)
library(data.table)
library(webshot)
library(coronavirus)
library(runner)
library(zoo)
library(DataCombine)
library(fastDummies)
library(car)
library(heatmaply)
library(htmlwidgets)
library(summarytools)
library(glmnet)
library(caret)
library(mlbench)
library(psych)
library(plm)
library(lmtest)
library(quantmod)
library(leaflet)
library(corrplot)
library(fBasics)
library(stargazer)
library(tseries)
library(vars)
library(dplyr)
library(haven)
# Remove factor variables for government responses.
datweekly_cs <- Final_Data_Country
datweekly_cs<-datweekly_cs %>% dplyr::select(-contains("Flag"))
datweekly_cs<-datweekly_cs %>% dplyr::select(-contains("Lagged"))
datweekly_cs$latitude <- NULL
datweekly_cs$longitude <- NULL
# Merge global preference survey data to Final_Data_Country
gps.country <- read_dta("data/global_preference_survey_country.dta")
# Change country names for merge
gps.country[which(gps.country$country == "South Korea"),"country"] <- "Korea, South"
gps.country[which(gps.country$country == "Czech Republic"),"country"] <- "Czechia"
gps.country[which(gps.country$country == "United States"),"country"] <- "US"
gps.individual <- read_dta("data/global_preference_survey_individual.dta")
# Change country names for merge
gps.individual[which(gps.individual$country == "South Korea"),"country"] <- "Korea, South"
gps.individual[which(gps.individual$country == "Czech Republic"),"country"] <- "Czechia"
gps.individual[which(gps.individual$country == "United States"),"country"] <- "US"
gps.individual$age <- as.character(gps.individual$age)
gps.individual[which(gps.individual$age == "99 99+"), "age"] <- "99"
gps.individual[which(gps.individual$age == "100 (Refused"), "age"] <- NA
gps.individual$age <- as.numeric(gps.individual$age)
gps.individual.pop65 <- gps.individual[which(!is.na(gps.individual$age) & gps.individual$age > 65), ]
gps.individual.pop65 <- gps.individual.pop65[which(!is.na(gps.individual.pop65$trust)),]
gps.country.pop65 <- ddply(gps.individual.pop65, "country", function(X) data.frame(wm.trust = weighted.mean(X$trust, X$wgt)))
colnames(gps.country)[1] <- "COUNTRY"
datweekly_cs <- merge(datweekly_cs, gps.country, by=c("COUNTRY"), all.x=TRUE)
colnames(gps.country.pop65)[1] <- "COUNTRY"
datweekly_cs <- merge(datweekly_cs, gps.country.pop65, by=c("COUNTRY"), all.x=TRUE)
datweekly_cs<-pdata.frame(datweekly_cs, index = c("COUNTRY", "Date"))
datweekly_cs$Date<-as.Date(datweekly_cs$Date,"%Y-%m-%d")
#reorder
datweekly_cs<-datweekly_cs[order(datweekly_cs$Date),]
datweekly_cs<-datweekly_cs[order(datweekly_cs$COUNTRY),]
colnames(datweekly_cs)[colnames(datweekly_cs) == "Total_Cases_Country"] <- "Total_Case_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "Total_Deceased_Country"] <- "Total_Death_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "New_Confirmed_Country"] <- "New_Case_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "New_Total_Deceased_Country"] <- "New_Death_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "Total_Mortality_Rate_Per_Capita"] <- "Total_Mortality_Rate"
colnames(datweekly_cs)[colnames(datweekly_cs) == "New_Mortality_Rate_Per_Capita"] <- "New_Mortality_Rate"
colnames(datweekly_cs)[colnames(datweekly_cs) == "Total_Cases_Country_Per_Capita"] <- "Total_Case_Rate"
colnames(datweekly_cs)[colnames(datweekly_cs) == "rolling_average_confirmed"] <- "RollingAverage_New_Case_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "rolling_average_deceased"] <- "RollingAverage_New_Death_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "total_rolling_average_mortality"] <- "RollingAverage_Total_Mortality_Rate"
colnames(datweekly_cs)[colnames(datweekly_cs) == "new_rolling_average_mortality"] <- "RollingAverage_New_Mortality_Rate"
colnames(datweekly_cs)[colnames(datweekly_cs) == "EUI_democracy"] <- "EIU_Democracy"
datweekly_cs$RollingAverage_Total_Mortality_Rate <- NULL
datweekly_cs$longitude <- NULL
datweekly_cs$latitude <- NULL
datweekly_cs$New_Case_Rate = datweekly_cs$New_Case_Country/datweekly_cs$Population
datweekly_cs <- datweekly_cs %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(RollingAverage_New_Case_Rate = frollmean(New_Case_Rate, 7,na.rm=TRUE))
datweekly_cs <- datweekly_cs %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(Total_Mortality_Rate_Growth = log(Total_Mortality_Rate) - Lag(log(Total_Mortality_Rate),7))
datweekly_cs <- datweekly_cs %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(Total_Case_Rate_Growth = log(Total_Case_Rate) - Lag(log(Total_Case_Rate),7))
datweekly_cs <- datweekly_cs %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(New_Case_Rate_Growth = log(RollingAverage_New_Case_Rate) - Lag(log(RollingAverage_New_Case_Rate),7))
datweekly_cs <- datweekly_cs %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(New_Mortality_Rate_Growth = log(RollingAverage_New_Mortality_Rate) - Lag(log(RollingAverage_New_Mortality_Rate),7))
datweekly_cs$respiratory_deaths_rate <- 1/3*(datweekly_cs$respiratory_deaths/datweekly_cs$Population)
datweekly_cs <- datweekly_cs %>%
dplyr::select(c("COUNTRY", "X1", "Date",
"Total_Case_Country", "Total_Death_Country", "New_Case_Country", "New_Death_Country", "Population",
"Total_Case_Rate", "Total_Mortality_Rate", "New_Case_Rate", "New_Mortality_Rate",
"RollingAverage_New_Case_Country", "RollingAverage_New_Death_Country",
"RollingAverage_New_Case_Rate", "RollingAverage_New_Mortality_Rate",
"Total_Case_Rate_Growth", "Total_Mortality_Rate_Growth",
"New_Case_Rate_Growth", "New_Mortality_Rate_Growth"), everything())
datweekly_cs$Total_Case_Rate_Growth[is.nan(datweekly_cs$Total_Case_Rate_Growth)] <- NA
datweekly_cs$Total_Mortality_Rate_Growth[is.nan(datweekly_cs$Total_Mortality_Rate_Growth)] <- NA
datweekly_cs$New_Case_Rate_Growth[is.nan(datweekly_cs$New_Case_Rate_Growth)] <- NA
datweekly_cs$New_Mortality_Rate_Growth[is.nan(datweekly_cs$New_Mortality_Rate_Growth)] <- NA
datweekly_cs$New_Mortality_Rate_Growth[is.infinite(-datweekly_cs$New_Mortality_Rate_Growth)] <- Inf
colnames(datweekly_cs)
## [1] "COUNTRY"
## [2] "X1"
## [3] "Date"
## [4] "Total_Case_Country"
## [5] "Total_Death_Country"
## [6] "New_Case_Country"
## [7] "New_Death_Country"
## [8] "Population"
## [9] "Total_Case_Rate"
## [10] "Total_Mortality_Rate"
## [11] "New_Case_Rate"
## [12] "New_Mortality_Rate"
## [13] "RollingAverage_New_Case_Country"
## [14] "RollingAverage_New_Death_Country"
## [15] "RollingAverage_New_Case_Rate"
## [16] "RollingAverage_New_Mortality_Rate"
## [17] "Total_Case_Rate_Growth"
## [18] "Total_Mortality_Rate_Growth"
## [19] "New_Case_Rate_Growth"
## [20] "New_Mortality_Rate_Growth"
## [21] "C1_School.closing"
## [22] "C2_Workplace.closing"
## [23] "C3_Cancel.public.events"
## [24] "C4_Restrictions.on.gatherings"
## [25] "C5_Close.public.transport"
## [26] "C6_Stay.at.home.requirements"
## [27] "C7_Restrictions.on.internal.movement"
## [28] "C8_International.travel.controls"
## [29] "E1_Income.support"
## [30] "E2_Debt.contract.relief"
## [31] "E3_Fiscal.measures"
## [32] "H1_Public.information.campaigns"
## [33] "H2_Testing.policy"
## [34] "H3_Contact.tracing"
## [35] "H4_Emergency.investment.in.healthcare"
## [36] "H5_Investment.in.vaccines"
## [37] "M1_Wildcard"
## [38] "StringencyIndex"
## [39] "LegacyStringencyIndex"
## [40] "Oxford_Cases"
## [41] "Oxford_Deaths"
## [42] "Latitude"
## [43] "Longitude"
## [44] "prop65"
## [45] "propurban"
## [46] "popdensity"
## [47] "driving"
## [48] "walking"
## [49] "transit"
## [50] "arrivals"
## [51] "departures"
## [52] "vul_emp"
## [53] "GNI"
## [54] "health_exp"
## [55] "pollution"
## [56] "EIU_Democracy"
## [57] "freedom_house"
## [58] "cellular_sub"
## [59] "milex"
## [60] "milper"
## [61] "irst"
## [62] "pec"
## [63] "cinc"
## [64] "endocrine_deaths"
## [65] "blood_pressure_deaths"
## [66] "respiratory_deaths"
## [67] "govt_accountability"
## [68] "political_stability"
## [69] "govt_effectiveness"
## [70] "regulatory_quality"
## [71] "rule_of_law"
## [72] "control_of_corruption"
## [73] "isocode"
## [74] "patience"
## [75] "risktaking"
## [76] "posrecip"
## [77] "negrecip"
## [78] "altruism"
## [79] "trust"
## [80] "wm.trust"
## [81] "respiratory_deaths_rate"
country <- c("")
# Mortality
date.first.death <- c(0)
date.peak.mortality <- c(0)
days.to.peak.mortality <- c(0)
peak.or.no.mortality <- c(0)
peak.cum.mortality <- c(0)
peak.new.mortality <- c(0)
early.mortality <- c(0)
early.mortality.growth<-c(0)
# Case
date.first.case <- c(0)
date.peak.case <- c(0)
days.to.peak.case <- c(0)
peak.or.no.case <- c(0)
peak.cum.case <- c(0)
peak.new.case <- c(0)
early.case <- c(0)
early.case.growth <- c(0)
# Stringency Index
early.stringency.sum <- c(0)
peak.stringency <- c(0)
first.stringency <- c(0)
date.first.stringency <- c(0)
date.peak.stringency <- c(0)
early.stringency.average <- c(0)
# Mobility
early.mobility.walking<-c(0)
dems_data<-aggregate(data=datweekly_cs,cbind(prop65,propurban,popdensity,vul_emp,health_exp,GNI,pollution,Latitude,Longitude,EIU_Democracy)~COUNTRY,FUN=mean,na.rm=T)
for(i in 1:length(unique(dems_data$COUNTRY))){
datweekly_csrevised<-subset(datweekly_cs,COUNTRY==unique(dems_data$COUNTRY)[i])
country[i] <- as.character(unique(dems_data$COUNTRY)[i])
datweekly_csrevised<-datweekly_csrevised[order(datweekly_csrevised$Date),]
datweekly_csrevised<-datweekly_csrevised[order(datweekly_csrevised$COUNTRY),]
datweekly_csrevised <- datweekly_csrevised %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(RollingAverage_Walking = frollmean(walking, 7))
datweekly_csrevised <- datweekly_csrevised %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(RollingAverage_StringencyIndex = frollmean(StringencyIndex, 7))
# Mortality
date.first.death[i]<-as.character(datweekly_csrevised$Date[which(datweekly_csrevised$Total_Death_Country>0)[1]]) # Date of first death
peak.new.mortality[i]<-max(datweekly_csrevised$RollingAverage_New_Mortality_Rate,na.rm = TRUE) # Peak new mortality
date.peak.mortality[i]<-as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate==peak.new.mortality[i]))]) # Date of peak new mortality
days.to.peak.mortality[i]<-as.Date(date.peak.mortality[i])-as.Date(date.first.death[i]) # Days from first death to peak new mortality
peak.cum.mortality[i]<-datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate==peak.new.mortality[i])] # Total mortality rate at the peak
peak.or.no.mortality[i]<-ifelse(date.peak.mortality[i]==max(datweekly_csrevised$Date),0,1)
early.mortality[i]<-datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$Total_Death_Country>0)[7]] # Total mortality in the first week since first death
early.mortality.growth[i]<-log(datweekly_csrevised$RollingAverage_New_Mortality_Rate[which(datweekly_csrevised$Total_Death_Country>0)[7]]) -
log(datweekly_csrevised$RollingAverage_New_Mortality_Rate[which(datweekly_csrevised$Total_Death_Country>0)[1]])# Growth rate of new mortality rate in the first week since first death
# Case
date.first.case[i]<-as.character(datweekly_csrevised$Date[which(datweekly_csrevised$Total_Case_Country>0)[1]]) # Date of first case
peak.new.case[i]<-max(datweekly_csrevised$RollingAverage_New_Case_Rate, na.rm = TRUE) # Peak new case
date.peak.case[i]<-as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Case_Rate==peak.new.case[i]))]) # Date of peak new case
days.to.peak.case[i]<-as.Date(date.peak.case[i])-as.Date(date.first.case[i]) # Days from first case to peak new case
peak.cum.case[i]<-datweekly_csrevised$Total_Case_Rate[which.max(datweekly_csrevised$RollingAverage_New_Case_Rate==peak.new.case[i])] # Toal case rate at the peak
peak.or.no.case[i]<-ifelse(date.peak.case[i]==max(datweekly_csrevised$Date),0,1)
early.case[i]<-datweekly_csrevised$Total_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[7]] # Total case rate in the first week since first case
if (datweekly_csrevised$RollingAverage_New_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[1]] == 0){
early.case.growth[i]<-log(datweekly_csrevised$RollingAverage_New_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[7]]) - log(datweekly_csrevised$Total_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[1]])# Growth rate of new case rate in the first week since first case
}else{
early.case.growth[i]<-log(datweekly_csrevised$RollingAverage_New_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[7]]) - log(datweekly_csrevised$RollingAverage_New_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[1]])# Growth rate of new case rate in the first week since first case
}
# Stringency Index
early.stringency.sum[i] <- sum(datweekly_csrevised$StringencyIndex[which(datweekly_csrevised$Date<date.first.death[i] & !is.na(datweekly_csrevised$StringencyIndex))]) # Sum of stringency index prior to first death
early.stringency.average[i] <- mean(datweekly_csrevised$StringencyIndex[which(datweekly_csrevised$Date<date.first.death[i] & !is.na(datweekly_csrevised$StringencyIndex))]) # Average of stringency index prior to first death
# early.stringency[i]<-b$RollingAverage_StringencyIndex[which(b$Total_Death_Country>0)[1]-7] #Weekly average stringency index in the week prior to first death
# early.stringency[i]<-b$StringencyIndex[which(b$Total_Death_Country>0)[1]-10] #Weekly average stringency index in the week prior to first death
first.stringency[i] <- datweekly_csrevised$StringencyIndex[which(datweekly_csrevised$StringencyIndex > 0)[1]] # First SI, no matter before or after the first case
# first.stringency[i] <- b$StringencyIndex[which(b$C1_School.closing > 0 | b$C2_Workplace.closing > 0 |
# b$C3_Cancel.public.events > 0 | b$C4_Restrictions.on.gatherings > 0 |
# b$C5_Close.public.transport > 0 | b$C6_Stay.at.home.requirements > 0 |
# b$C7_Restrictions.on.internal.movement > 0)[1]] # First SI, no matter before or after the first case or death
peak.stringency[i] <- max(datweekly_csrevised$StringencyIndex,na.rm=T) # Peak SI
date.first.stringency[i] <- as.character(datweekly_csrevised$Date[which(datweekly_csrevised$StringencyIndex > 0)[1]]) # Date of first SI
# date.first.stringency[i] <- as.character(b$Date[which(b$C1_School.closing > 0 | b$C2_Workplace.closing > 0 |
# b$C3_Cancel.public.events > 0 | b$C4_Restrictions.on.gatherings > 0 |
# b$C5_Close.public.transport > 0 | b$C6_Stay.at.home.requirements > 0 |
# b$C7_Restrictions.on.internal.movement > 0)[1]]) # Date of first SI
if (peak.stringency[i] == -Inf){
date.peak.stringency[i] <- -Inf
}else{
date.peak.stringency[i] <- as.character(datweekly_csrevised$Date[which.max(datweekly_csrevised$StringencyIndex)])
}
# Mobility
early.mobility.walking[i]<-datweekly_csrevised$RollingAverage_Walking[which(datweekly_csrevised$Total_Death_Country>0)[1]-1] # Weekly average walking mobility in the week prior to first death
}
## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in max(datweekly_csrevised$StringencyIndex, na.rm = T): no non-missing
## arguments to max; returning -Inf
## Warning in max(datweekly_csrevised$StringencyIndex, na.rm = T): no non-missing
## arguments to max; returning -Inf
## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in max(datweekly_csrevised$StringencyIndex, na.rm = T): no non-missing
## arguments to max; returning -Inf
## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length
## Warning in date.peak.case[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Case_Rate
## == : number of items to replace is not a multiple of replacement length
stringency.delta <- (peak.stringency-first.stringency)/as.numeric(as.Date(date.peak.stringency)-as.Date(date.first.stringency))
log.early.stringency.sum <- log(early.stringency.sum)
log.early.stringency.sum[which(is.infinite(-log.early.stringency.sum))] <- NA
how.early.stringency <- as.numeric(as.Date(date.first.death)-as.Date(date.first.stringency))
# early.stringency.average <- early.stringency/how.early.stringency
log.peak.mortality.to.duration <- log(peak.new.mortality)/as.numeric(days.to.peak.mortality)
log.peak.case.to.duration <- log(peak.new.case)/as.numeric(days.to.peak.case)
# set NA early stringency to 0
# early.string[is.na(early.string)]<-0
surv.dat_weekly<-data.frame(country,days.to.peak.mortality,peak.or.no.mortality, days.to.peak.case,peak.or.no.case, early.mortality,early.mortality.growth, early.case,early.case.growth, peak.cum.mortality,peak.cum.case,peak.new.mortality,peak.new.case, log.peak.mortality.to.duration,log.peak.case.to.duration, early.stringency.average,early.stringency.sum,log.early.stringency.sum,how.early.stringency,stringency.delta,peak.stringency, early.mobility.walking,dems_data[,-1])
#remove vietnam
surv.dat_weekly<-surv.dat_weekly[-nrow(surv.dat_weekly),]
surv.dat_weekly<-mutate(surv.dat_weekly,stringent=ifelse(early.stringency.average>7.77,"SI>7.77","SI<7.77"),stringent=factor(stringent))
### - peak new mortality -
cs.new.mortality <- lm(log(peak.new.mortality) ~
log(early.mortality)+early.mortality.growth+
early.stringency.average+how.early.stringency+
stringency.delta+early.mobility.walking+
prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly,na.action = "na.exclude")
se.new.mortality<-coeftest(cs.new.mortality, vcov = vcovHC(cs.new.mortality, type = "HC1"))
surv.dat_weekly$res.new.mortality = resid(cs.new.mortality)
### - peak new mortality to duration ratio -
cs.peak.duration.ratio<- lm(log.peak.mortality.to.duration ~
log(early.mortality)+early.mortality.growth+
early.stringency.average+how.early.stringency+
stringency.delta+early.mobility.walking+
prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly,na.action = "na.exclude")
se.peak.duration.ratio<-coeftest(cs.peak.duration.ratio, vcov = vcovHC(cs.peak.duration.ratio, type = "HC1"))
surv.dat_weekly$res.peak.duration.ratio = resid(cs.peak.duration.ratio)
### - survival analysis on pandemic duration to first peak
cox.death<-coxph(Surv(days.to.peak.mortality,peak.or.no.mortality) ~
log(peak.new.mortality)+log(early.mortality)+early.mortality.growth+
early.stringency.average+how.early.stringency+
stringency.delta+early.mobility.walking+
prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly)
test.death<-cox.zph(cox.death)$table[nrow(cox.zph(cox.death)$table),ncol(cox.zph(cox.death)$table)]
stargazer(cs.new.mortality,cs.peak.duration.ratio,cox.death,
se=list(se.new.mortality[,2],se.peak.duration.ratio[,2]),type="html",out=("Table_cs_reg_output_avgSI.htm"),intercept.bottom = FALSE,
dep.var.labels=c("Log(Peak New Mortality Rate)","Log(Peak New Mortality Rate)-to-PD Ratio","Survival Probability of Mortality Peaking at Time (t)"),
covariate.labels=c("Intercept","Log(Peak Mortality)","Log(Early Mortality)","Early Mortality Growth",
"Early SI","Days between First SI and First Death",
"Stringency Delta","Early Mobility",
"Prop. 65+","Prop. Urban","Pop. Density","Vulnerable Emp.", "Log(GNI)",
"EIU Democracy","Latitude:Longitude"),
df = FALSE,omit.stat =c("max.rsq","logrank"),
notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.",
"PH Test refers to testing the proportional hazards assumption (Grambsch and Therneau (1994)).",
"Null hypothesis is the assumption is not violated.",
"Standard errors in linear models are Heteroscedastic-Robust standard errors."),
notes.append=F,notes.align ="l",font.size = "tiny",
title=("Cross-Country Regression: Explaining Differences in the Empirical Shape of First-Wave Mortality Rates"),
add.lines = list(c("PH Test p-value",NA,NA,round(test.death,3))),
table.layout = "-ldm#-t=sa-n")
km_fit <- survfit(Surv(days.to.peak.mortality,peak.or.no.mortality) ~1, data=surv.dat_weekly)
p1<-autoplot(km_fit)+theme_bw()+xlab("Number of Days")+ylab("Probability that Mortality Peak is Yet to Come")
km_fit2<-survfit(Surv(days.to.peak.mortality,peak.or.no.mortality)~stringent, data=surv.dat_weekly)
p2<-autoplot(km_fit2)+theme_bw()+xlab("Number of Days")+ylab("Probability that Mortality Peak is Yet to Come")+theme(legend.position = c(0.2, 0.2),legend.title=element_blank())
p3<-ggplot(data=surv.dat_weekly,aes(x=early.stringency.average,fill=stringent))+geom_histogram(color="black",alpha=.5)+theme_bw()+xlab("Average SI from the First Non-Zero SI to the First Death")+ylab("Frequency of Countries")+theme(legend.title = element_blank(),legend.position = c(0.7,0.6))
Plot_76 <- grid.arrange(p1, p2, p3, nrow=1)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (stat_bin).
# g <- arrangeGrob(p1,p2,p3,nrow=1)
cor.surv.dat <- surv.dat_weekly[,c("days.to.peak.mortality","early.mortality","early.mortality.growth",
"peak.new.mortality","log.peak.mortality.to.duration",
"early.stringency.average","how.early.stringency","stringency.delta",
"early.mobility.walking",
"prop65","propurban","popdensity","vul_emp","health_exp","GNI","pollution","Latitude","Longitude","EIU_Democracy")]
colnames(cor.surv.dat)<-c("PD to Peak Mortality", "Early Mortality", "Early Mortality Growth",
"Peak New Mortality", "Logged Peak Mortality-to-PD",
"Early SI","Days from First SI to First Death", "Stringency Delta",
"Early Mobility",
"Prop. 65+", "Prop. Urban", "Pop. Density",
"Vulnerable Emp.","Health Exp.","GNI","Pollution","Latitude","Longitude","Democracy")
cormat<-round(cor(cor.surv.dat,use = "complete.obs"), 2)
# Here, I get the lower triangle of the correlation matrix
get_lower_tri<-function(cormat){
cormat[upper.tri(cormat)] <- NA
return(cormat)
}
# Now, I get the upper triangle of the correlation matrix
get_upper_tri <- function(cormat){
cormat[lower.tri(cormat)]<- NA
return(cormat)
}
reorder_cormat <- function(cormat){
# Use correlation between variables as distance
dd <- as.dist((1-cormat)/2)
hc <- hclust(dd)
cormat <-cormat[hc$order, hc$order]
}
cormat <- reorder_cormat(cormat)
melted_cormat <- reshape2::melt(get_upper_tri(cormat), na.rm = TRUE)
### Generate the Static Correlation Map
ggheatmap <- ggplot(data = melted_cormat, aes(x=Var2, y=Var1, fill=value)) +
geom_tile(color = "black") +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45)) +
theme(axis.text.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0))) +
theme(axis.text.x = element_text(hjust=1))+coord_fixed()
Plot_77 <- ggheatmap +
geom_text(aes(Var2, Var1, label = value), color = "black", size = 1.75) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text=element_text(size=7),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(0.4, 0.8),
legend.direction = "horizontal",
legend.title = element_text(size = 6),
legend.text=element_text(size=6))+
guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
title.position = "top", title.hjust = 0.5))
# Print the heatmap
print(Plot_77)
cor.policy.dat <- datweekly_cs[,c("C1_School.closing",
"C2_Workplace.closing",
"C3_Cancel.public.events",
"C4_Restrictions.on.gatherings",
"C5_Close.public.transport",
"C6_Stay.at.home.requirements",
"C7_Restrictions.on.internal.movement",
"C8_International.travel.controls",
"H1_Public.information.campaigns",
"StringencyIndex")]
colnames(cor.policy.dat) <- c("School Closing",
"Workplace Closing",
"Cancel Public Events",
"Gathering Restrictions",
"Close Public Transport",
"Stay at Home",
"Internal Movement Restrictions",
"International Travel Controls",
"Public Information Campaigns",
"Stringency Index")
cormat<-round(cor(cor.policy.dat,use = "complete.obs"), 2)
# Here, I get the lower triangle of the correlation matrix
get_lower_tri<-function(cormat){
cormat[upper.tri(cormat)] <- NA
return(cormat)
}
# Now, I get the upper triangle of the correlation matrix
get_upper_tri <- function(cormat){
cormat[lower.tri(cormat)]<- NA
return(cormat)
}
reorder_cormat <- function(cormat){
# Use correlation between variables as distance
dd <- as.dist((1-cormat)/2)
hc <- hclust(dd)
cormat <-cormat[hc$order, hc$order]
}
cormat <- reorder_cormat(cormat)
melted_cormat <- reshape2::melt(get_upper_tri(cormat), na.rm = TRUE)
### Generate the Static Correlation Map
ggheatmap <- ggplot(data = melted_cormat, aes(x=Var2, y=Var1, fill=value)) +
geom_tile(color = "black") +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45)) +
theme(axis.text.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0))) +
theme(axis.text.x = element_text(hjust=1))+coord_fixed()
Plot_78 <- ggheatmap +
geom_text(aes(Var2, Var1, label = value), color = "black", size = 1.75) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text=element_text(size=7),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(0.6, 0.7),
legend.direction = "horizontal",
legend.title = element_text(size = 7),
legend.text=element_text(size=7),
plot.caption = element_text(size = 7, hjust=0))+
guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
title.position = "top", title.hjust = 0.5))
# Print the heatmap
print(Plot_78)
cor.sum.dat <- surv.dat_weekly[,c("days.to.peak.mortality","early.mortality","early.mortality.growth",
"peak.new.mortality","log.peak.mortality.to.duration",
"early.stringency.average","how.early.stringency","stringency.delta",
"early.mobility.walking",
"prop65","propurban","popdensity","vul_emp","health_exp","GNI","pollution","Latitude","Longitude","EIU_Democracy")]
cor.sum.dat$early.mortality <- cor.sum.dat$early.mortality * 10000000
cor.sum.dat$peak.new.mortality <- cor.sum.dat$peak.new.mortality * 10000000
cor.sum.dat$GNI <- log(cor.sum.dat$GNI)
colnames(cor.sum.dat)<-c("PD to Peak Mortality", "Early Mortality", "Early Mortality Growth",
"Peak New Mortality",
"Logged Peak Mortality-to-PD",
"Early SI","Days from First SI to First Death", "Stringency Delta",
"Early Mobility",
"Prop. 65+", "Prop. Urban", "Pop. Density",
"Vulnerable Employment","Health Expenditure","Log(GNI)","Pollution","Latitude","Longitude","Democracy")
sum.stats <- t(round(basicStats(cor.sum.dat),3))
sum.stats <- data.frame(sum.stats[,c("nobs", "NAs", "Minimum", "Maximum", "1. Quartile", "3. Quartile", "Mean", "Median", "Stdev")])
sum.stats$N <- sum.stats$nobs - sum.stats$NAs
sum.stats <- sum.stats[,-c(1,2)]
sum.stats <- sum.stats %>%
dplyr::select(c("N", "Minimum", "Maximum", "Mean", "Median", "Stdev"), everything())
colnames(sum.stats) <- c("N", "Minimum", "Maximum", "Mean", "Median", "S.D.", "25 Percentile", "75 Percentile")
peak.stringency.finite <- cor.sum.dat$`Peak Stringency`[which(is.finite(cor.sum.dat$`Peak Stringency`))]
stargazer(sum.stats,type="html",summary = FALSE, out=("Table_summary_country_char.htm"),title="Country Characteristics: Summary")
##
## <table style="text-align:center"><caption><strong>Country Characteristics: Summary</strong></caption>
## <tr><td colspan="9" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>N</td><td>Minimum</td><td>Maximum</td><td>Mean</td><td>Median</td><td>S.D.</td><td>25 Percentile</td><td>75 Percentile</td></tr>
## <tr><td colspan="9" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">PD.to.Peak.Mortality</td><td>58</td><td>3</td><td>85</td><td>33.983</td><td>33.500</td><td>15.550</td><td>26</td><td>40</td></tr>
## <tr><td style="text-align:left">Early.Mortality</td><td>58</td><td>0.022</td><td>67.944</td><td>8.055</td><td>2.865</td><td>13.840</td><td>0.863</td><td>8.204</td></tr>
## <tr><td style="text-align:left">Early.Mortality.Growth</td><td>58</td><td>0</td><td>3.611</td><td>1.369</td><td>1.386</td><td>0.990</td><td>0.693</td><td>1.946</td></tr>
## <tr><td style="text-align:left">Peak.New.Mortality</td><td>58</td><td>0.368</td><td>287.696</td><td>32.445</td><td>8.339</td><td>55.498</td><td>2.032</td><td>29.007</td></tr>
## <tr><td style="text-align:left">Logged.Peak.Mortality.to.PD</td><td>58</td><td>-5.492</td><td>-0.187</td><td>-0.655</td><td>-0.417</td><td>0.907</td><td>-0.514</td><td>-0.322</td></tr>
## <tr><td style="text-align:left">Early.SI</td><td>55</td><td>0</td><td>27.450</td><td>7.773</td><td>6.309</td><td>5.891</td><td>3.633</td><td>10.192</td></tr>
## <tr><td style="text-align:left">Days.from.First.SI.to.First.Death</td><td>55</td><td>-8</td><td>79</td><td>32.855</td><td>32</td><td>22.047</td><td>15.500</td><td>51</td></tr>
## <tr><td style="text-align:left">Stringency.Delta</td><td>55</td><td>0.479</td><td>5.930</td><td>1.713</td><td>1.301</td><td>1.181</td><td>0.969</td><td>1.796</td></tr>
## <tr><td style="text-align:left">Early.Mobility</td><td>51</td><td>26.130</td><td>149.684</td><td>92.657</td><td>96.114</td><td>31.365</td><td>65.111</td><td>111.594</td></tr>
## <tr><td style="text-align:left">Prop..65.</td><td>58</td><td>1.085</td><td>27.576</td><td>14.004</td><td>15.212</td><td>6.442</td><td>8.479</td><td>19.410</td></tr>
## <tr><td style="text-align:left">Prop..Urban</td><td>58</td><td>34.030</td><td>100</td><td>76.513</td><td>80.239</td><td>15.141</td><td>67.027</td><td>87.216</td></tr>
## <tr><td style="text-align:left">Pop..Density</td><td>58</td><td>3.249</td><td>7,952.998</td><td>295.055</td><td>107.557</td><td>1,047.129</td><td>32.179</td><td>213.004</td></tr>
## <tr><td style="text-align:left">Vulnerable.Employment</td><td>58</td><td>0.144</td><td>74.270</td><td>16.513</td><td>10.662</td><td>15.229</td><td>7.396</td><td>21.522</td></tr>
## <tr><td style="text-align:left">Health.Expenditure</td><td>58</td><td>69.293</td><td>10,246.140</td><td>2,615.687</td><td>1,589.132</td><td>2,453.654</td><td>666.110</td><td>4,336.231</td></tr>
## <tr><td style="text-align:left">Log.GNI.</td><td>58</td><td>7.611</td><td>11.343</td><td>9.965</td><td>10.147</td><td>0.969</td><td>9.256</td><td>10.753</td></tr>
## <tr><td style="text-align:left">Pollution</td><td>58</td><td>5.861</td><td>91.187</td><td>22.256</td><td>16.030</td><td>21.446</td><td>10.392</td><td>21.295</td></tr>
## <tr><td style="text-align:left">Latitude</td><td>58</td><td>-40.901</td><td>64.963</td><td>31.292</td><td>38.027</td><td>27.425</td><td>23.477</td><td>51</td></tr>
## <tr><td style="text-align:left">Longitude</td><td>58</td><td>-106.347</td><td>174.886</td><td>24.668</td><td>19.324</td><td>61.076</td><td>2.778</td><td>46.881</td></tr>
## <tr><td style="text-align:left">Democracy</td><td>58</td><td>1.930</td><td>9.870</td><td>7.088</td><td>7.510</td><td>1.991</td><td>6.605</td><td>8.262</td></tr>
## <tr><td colspan="9" style="border-bottom: 1px solid black"></td></tr></table>
datweekly_cs <- datweekly_cs %>%
dplyr::group_by(COUNTRY) %>%
dplyr::mutate(First_Date_Mortality = as.character(Date[which(Total_Death_Country>0)[1]]))
datweekly_cs$Days_Since_First_Death <- as.Date(as.character(datweekly_cs$Date))-as.Date(as.character(datweekly_cs$First_Date_Mortality))
datweekly_csnormalized <- datweekly_cs[which(datweekly_cs$First_Date_Mortality>-1),]
Plot_79 <- plot_ly(datweekly_csnormalized, x=~Days_Since_First_Death, y=~RollingAverage_New_Mortality_Rate) %>%
add_lines(linetype = ~COUNTRY) %>%
layout(xaxis = list(range = c(0, 50)))
Plot_79
## Warning: plotly.js only supports 6 different linetypes
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datweekly_csnormalized_v0 <- datweekly_csnormalized[which(datweekly_cs$COUNTRY == "Czechia"),]
Mortality_curve_v0 <- ggplot(data=datweekly_csnormalized_v0, aes(x=Days_Since_First_Death, y=RollingAverage_New_Mortality_Rate)) +
geom_line(color="blue", size = 0.75) + geom_segment(aes(x=23, xend=23, y=0, yend=max(datweekly_csnormalized_v0$RollingAverage_New_Mortality_Rate[which(!is.na(datweekly_csnormalized_v0$RollingAverage_New_Mortality_Rate))])), size=1, colour="blue", linetype="dotted") + geom_ribbon(data = datweekly_csnormalized_v0 %>% dplyr::filter(Days_Since_First_Death < 24), aes(ymin = 0, ymax = datweekly_csnormalized_v0$RollingAverage_New_Mortality_Rate[which(datweekly_csnormalized_v0$Days_Since_First_Death<24)]), fill = "red", alpha = .5) + xlim(0, 50) + labs(x = "Days since the first death", y = "Daily new mortality rate, 7-day rolling average") + theme_bw() + theme(axis.text.x = element_text(size = 10), axis.text.y = element_text(size = 10), axis.title = element_text(size = 14))
Mortality_curve_v0
## Warning: Removed 82 row(s) containing missing values (geom_path).
datweekly_csnormalized_v1 <- datweekly_csnormalized[which(datweekly_cs$COUNTRY == "Hungary" | datweekly_cs$COUNTRY == "Norway"),]
Mortality_curve_v1 <- ggplot(data=datweekly_csnormalized_v1, aes(x=Days_Since_First_Death, y=RollingAverage_New_Mortality_Rate, group=COUNTRY)) +
geom_line(aes(color=COUNTRY), size = 0.75) + xlim(0, 50) + labs(x = "Days since the first death", y = "Daily new mortality rate, 7-day rolling average") + theme_bw() + theme(legend.position = c(0.2, 0.8), legend.title = element_blank(), legend.text=element_text(size=8))
Mortality_curve_v1
## Warning: Removed 149 row(s) containing missing values (geom_path).
datweekly_csnormalized_v2 <- datweekly_csnormalized[which(datweekly_cs$COUNTRY == "Denmark" | datweekly_cs$COUNTRY == "Norway"),]
Mortality_curve_v2 <- ggplot(data=datweekly_csnormalized_v2, aes(x=Days_Since_First_Death, y=RollingAverage_New_Mortality_Rate, group=COUNTRY)) +
geom_line(aes(color=COUNTRY), size = 0.75) + xlim(0, 50) + labs(x = "Days since the first death", y = "Daily new mortality rate, 7-day rolling average") + theme_bw() + theme(legend.position = c(0.2, 0.8), legend.title = element_blank(), legend.text=element_text(size=8))
Mortality_curve_v2
## Warning: Removed 148 row(s) containing missing values (geom_path).
datweekly_csnormalized_v3 <- datweekly_csnormalized[which(datweekly_cs$COUNTRY == "Austria" | datweekly_cs$COUNTRY == "Estonia" | datweekly_cs$COUNTRY == "Greece"),]
Mortality_curve_v3 <- ggplot(data=datweekly_csnormalized_v3, aes(x=Days_Since_First_Death, y=RollingAverage_New_Mortality_Rate, group=COUNTRY)) +
geom_line(aes(color=COUNTRY), size = 0.75) + xlim(0, 50) + labs(x = "Days since the first death", y = "Daily new mortality rate, 7-day rolling average") + theme_bw() + theme(legend.position = c(0.15, 0.85),legend.title = element_blank(), legend.text=element_text(size=8))
Mortality_curve_v3
## Warning: Removed 228 row(s) containing missing values (geom_path).
Plot_80 <- grid.arrange(Mortality_curve_v1, Mortality_curve_v2, Mortality_curve_v3, nrow=1)
## Warning: Removed 149 row(s) containing missing values (geom_path).
## Warning: Removed 148 row(s) containing missing values (geom_path).
## Warning: Removed 228 row(s) containing missing values (geom_path).
Plot_80
## TableGrob (1 x 3) "arrange": 3 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
### - peak new mortality -
cs.new.mortality <- lm(log(peak.new.mortality) ~
log(early.mortality)+early.mortality.growth+
early.stringency.average+how.early.stringency+
stringency.delta+
prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly,na.action = "na.exclude")
se.new.mortality<-coeftest(cs.new.mortality, vcov = vcovHC(cs.new.mortality, type = "HC1"))
surv.dat_weekly$res.new.mortality = resid(cs.new.mortality)
### - peak new mortality to duration ratio -
cs.peak.duration.ratio<- lm(log.peak.mortality.to.duration ~
log(early.mortality)+early.mortality.growth+
early.stringency.average+how.early.stringency+
stringency.delta+
prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly,na.action = "na.exclude")
se.peak.duration.ratio<-coeftest(cs.peak.duration.ratio, vcov = vcovHC(cs.peak.duration.ratio, type = "HC1"))
surv.dat_weekly$res.peak.duration.ratio = resid(cs.peak.duration.ratio)
### - survival analysis on pandemic duration to first peak
cox.death<-coxph(Surv(days.to.peak.mortality,peak.or.no.mortality) ~
log(peak.new.mortality)+log(early.mortality)+early.mortality.growth+
early.stringency.average+how.early.stringency+
stringency.delta+
prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly)
test.death<-cox.zph(cox.death)$table[nrow(cox.zph(cox.death)$table),ncol(cox.zph(cox.death)$table)]
require(maps)
## Loading required package: maps
##
## Attaching package: 'maps'
## The following object is masked from 'package:plyr':
##
## ozone
## The following object is masked from 'package:purrr':
##
## map
require(viridis)
world_map <- map_data("world")
ggplot(world_map, aes(x = long, y = lat, group = group)) +
geom_polygon(fill="lightgray", colour = "white")
colnames(world_map)[1] <- "Longitude"
colnames(world_map)[2] <- "Latitude"
world_map$region[world_map$region == "Czech Republic"] <- "Czechia"
world_map$region[world_map$region == "USA"] <- "US"
world_map$region[world_map$region == "UK"] <- "United Kingdom"
world_map$region[world_map$region == "South Korea"] <- "Korea, South"
colnames(world_map)[5] <- "country"
surv.dat.map <- left_join(world_map, surv.dat_weekly, by = "country")
## Warning: Column `country` joining character vector and factor, coercing into
## character vector
colnames(surv.dat.map)[1] <- "Longitude"
colnames(surv.dat.map)[2] <- "Latitude"
# Finally, we can construct our initial marker map. First, I set different colors for each quantile.
surv.dat.map$abs.res.new.mortality <- abs(surv.dat.map$res.new.mortality)
p.res.new.mortality <- ggplot(surv.dat.map, aes(x = Longitude, y = Latitude)) +
geom_polygon(aes(group = group, fill = round(res.new.mortality,2)), color = "white") +
# geom_text(aes(label = round(cs.total.mortality.res,2)), data = surv.dat, size = 2)+
# scale_fill_viridis(option="C", begin = min(surv.dat$cs.total.mortality.res[!is.na(surv.dat$cs.total.mortality.res)]), end = max(surv.dat$cs.total.mortality.res[!is.na(surv.dat$cs.total.mortality.res)]))+
theme_void()+
labs(fill = "Residuals")+
theme(legend.text = element_text(size = 7),legend.title=element_text(size=8), legend.position = c(0.95,0.5))+
scale_fill_gradient(low = "red", high = "blue", na.value = "lightgrey",guide = "colourbar")+
guides(fill = guide_colourbar(barwidth = 1.1, barheight = 3.3))
p.res.new.mortality
surv.dat.sorted.new.mortality <- surv.dat_weekly[order(-surv.dat_weekly$res.new.mortality),]
res.new.mortality.rank <- surv.dat.sorted.new.mortality[!is.na(surv.dat.sorted.new.mortality$res.new.mortality),c("country", "res.new.mortality")]
colnames(res.new.mortality.rank) <- c("Country", "Residual")
rownames(res.new.mortality.rank) <- NULL
under.predicted.new.mortality <- res.new.mortality.rank[c(1:5),]
over.predicted.new.mortality <- res.new.mortality.rank[c(seq(nrow(res.new.mortality.rank),nrow(res.new.mortality.rank)-4,-1)),]
stargazer(under.predicted.new.mortality,type="html",rownames = FALSE, summary = FALSE, out=("Table_res_new_mortality_under_predicted.htm"))
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Country</td><td>Residual</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">France</td><td>2.715</td></tr>
## <tr><td style="text-align:left">Peru</td><td>2.207</td></tr>
## <tr><td style="text-align:left">Belgium</td><td>2.140</td></tr>
## <tr><td style="text-align:left">Kuwait</td><td>1.862</td></tr>
## <tr><td style="text-align:left">Ireland</td><td>1.589</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr></table>
stargazer(over.predicted.new.mortality,type="html",rownames = FALSE, summary = FALSE, out=("Table_res_new_mortality_over_predicted.htm"))
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Country</td><td>Residual</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Australia</td><td>-2.343</td></tr>
## <tr><td style="text-align:left">Japan</td><td>-1.670</td></tr>
## <tr><td style="text-align:left">Thailand</td><td>-1.580</td></tr>
## <tr><td style="text-align:left">Korea, South</td><td>-1.542</td></tr>
## <tr><td style="text-align:left">China</td><td>-1.479</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr></table>
require(maps)
require(viridis)
world_map <- map_data("world")
ggplot(world_map, aes(x = long, y = lat, group = group)) +
geom_polygon(fill="lightgray", colour = "white")
colnames(world_map)[1] <- "Longitude"
colnames(world_map)[2] <- "Latitude"
world_map$region[world_map$region == "Czech Republic"] <- "Czechia"
world_map$region[world_map$region == "USA"] <- "US"
world_map$region[world_map$region == "UK"] <- "United Kingdom"
world_map$region[world_map$region == "South Korea"] <- "Korea, South"
colnames(world_map)[5] <- "country"
surv.dat.map <- left_join(world_map, surv.dat_weekly, by = "country")
## Warning: Column `country` joining character vector and factor, coercing into
## character vector
colnames(surv.dat.map)[1] <- "Longitude"
colnames(surv.dat.map)[2] <- "Latitude"
surv.dat.map$abs.res.peak.duration.ratio <- abs(surv.dat.map$res.peak.duration.ratio)
p.res.peak.duration.ratio <- ggplot(surv.dat.map, aes(x = Longitude, y = Latitude)) +
geom_polygon(aes(group = group, fill = round(res.peak.duration.ratio,2)), color = "white") +
# geom_text(aes(label = round(cs.total.mortality.res,2)), data = surv.dat, size = 2)+
# scale_fill_viridis(option="C", begin = min(surv.dat$cs.total.mortality.res[!is.na(surv.dat$cs.total.mortality.res)]), end = max(surv.dat$cs.total.mortality.res[!is.na(surv.dat$cs.total.mortality.res)]))+
theme_void()+
labs(fill = "Residuals")+
theme(legend.text = element_text(size = 7),legend.title=element_text(size=8), legend.position = c(0.95,0.5))+
scale_fill_gradient(low = "green", high = "blue", na.value = "lightgrey",guide = "colourbar")+
guides(fill = guide_colourbar(barwidth = 1.1, barheight = 3.3))
p.res.peak.duration.ratio
surv.dat.sorted.peak.to.PD.ratio <- surv.dat_weekly[order(-surv.dat_weekly$res.peak.duration.ratio),]
res.ratio.rank <- surv.dat.sorted.peak.to.PD.ratio[!is.na(surv.dat.sorted.peak.to.PD.ratio$res.peak.duration.ratio),c("country", "res.peak.duration.ratio")]
colnames(res.ratio.rank) <- c("Country", "Residual")
rownames(res.ratio.rank) <- NULL
under.predicted.ratio <- res.ratio.rank[c(1:5),]
over.predicted.ratio <- res.ratio.rank[c(seq(nrow(res.ratio.rank),nrow(res.ratio.rank)-4,-1)),]
stargazer(under.predicted.ratio,type="html",rownames = FALSE, summary = FALSE, out=("Table_res_ratio_under_predicted.htm"))
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Country</td><td>Residual</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Egypt</td><td>0.682</td></tr>
## <tr><td style="text-align:left">Hungary</td><td>0.667</td></tr>
## <tr><td style="text-align:left">Kuwait</td><td>0.633</td></tr>
## <tr><td style="text-align:left">Peru</td><td>0.601</td></tr>
## <tr><td style="text-align:left">Belgium</td><td>0.546</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr></table>
stargazer(over.predicted.ratio,type="html",rownames = FALSE, summary = FALSE, out=("Table_res_ratio_over_predicted.htm"))
##
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Country</td><td>Residual</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Jordan</td><td>-3.256</td></tr>
## <tr><td style="text-align:left">Iceland</td><td>-1.176</td></tr>
## <tr><td style="text-align:left">India</td><td>-0.937</td></tr>
## <tr><td style="text-align:left">China</td><td>-0.474</td></tr>
## <tr><td style="text-align:left">Austria</td><td>-0.473</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr></table>